splunk-security_content/docs/detections.md

Splunk Security Content Detections

All the detections shipped to different Splunk products. Below is a breakdown by kind.

Cloud

details

• AWS Create Policy Version to allow all resources

• AWS CreateAccessKey

• AWS CreateLoginProfile

• AWS Cross Account Activity From Previously Unseen Account

• AWS Detect Users creating keys with encrypt policy without MFA

• AWS Detect Users with KMS keys performing encryption S3

• AWS Excessive Security Scanning

• AWS IAM AccessDenied Discovery Events

• AWS IAM Assume Role Policy Brute Force

• AWS IAM Delete Policy

• AWS IAM Failure Group Deletion

• AWS IAM Successful Group Deletion

• AWS Network Access Control List Created with All Open Ports

• AWS Network Access Control List Deleted

• AWS SAML Access by Provider User and Principal

• AWS SAML Update identity provider

• AWS SetDefaultPolicyVersion

• AWS UpdateLoginProfile

• Abnormally High Number Of Cloud Infrastructure API Calls

• Abnormally High Number Of Cloud Instances Destroyed

• Abnormally High Number Of Cloud Instances Launched

• Abnormally High Number Of Cloud Security Group API Calls

• Amazon EKS Kubernetes Pod scan detection

• Amazon EKS Kubernetes cluster scan detection

• Cloud API Calls From Previously Unseen User Roles

• Cloud Compute Instance Created By Previously Unseen User

• Cloud Compute Instance Created In Previously Unused Region

• Cloud Compute Instance Created With Previously Unseen Image

• Cloud Compute Instance Created With Previously Unseen Instance Type

• Cloud Instance Modified By Previously Unseen User

• Cloud Provisioning Activity From Previously Unseen City

• Cloud Provisioning Activity From Previously Unseen Country

• Cloud Provisioning Activity From Previously Unseen IP Address

• Cloud Provisioning Activity From Previously Unseen Region

• Detect AWS Console Login by New User

• Detect AWS Console Login by User from New City

• Detect AWS Console Login by User from New Country

• Detect AWS Console Login by User from New Region

• Detect GCP Storage access from a new IP

• Detect New Open GCP Storage Buckets

• Detect New Open S3 Buckets over AWS CLI

• Detect New Open S3 buckets

• Detect S3 access from a new IP

• Detect Spike in AWS Security Hub Alerts for EC2 Instance

• Detect Spike in AWS Security Hub Alerts for User

• Detect Spike in S3 Bucket deletion

• Detect Spike in blocked Outbound Traffic from your AWS

• GCP Detect gcploit framework

• GCP Kubernetes cluster pod scan detection

• High Number of Login Failures from a single source

• Kubernetes AWS detect suspicious kubectl calls

• New container uploaded to AWS ECR

• O365 Add App Role Assignment Grant User

• O365 Added Service Principal

• O365 Bypass MFA via Trusted IP

• O365 Disable MFA

• O365 Excessive Authentication Failures Alert

• O365 Excessive SSO logon errors

• O365 New Federated Domain Added

• O365 PST export alert

• O365 Suspicious Admin Email Forwarding

• O365 Suspicious Rights Delegation

• O365 Suspicious User Email Forwarding

• aws detect attach to role policy

• aws detect permanent key creation

• aws detect role creation

• aws detect sts assume role abuse

• aws detect sts get session token abuse

Endpoint

details

• Access LSASS Memory for Dump Creation

• Account Discovery With Net App

• Allow Inbound Traffic By Firewall Rule Registry

• Allow Inbound Traffic In Firewall Rule

• Anomalous usage of 7zip

• Any Powershell DownloadFile

• Any Powershell DownloadString

• Applying Stolen Credentials via Mimikatz modules

• Applying Stolen Credentials via PowerSploit modules

• Assessment of Credential Strength via DSInternals modules

• Attempt To Add Certificate To Untrusted Store

• Attempt To Stop Security Service

• Attempted Credential Dump From Registry via Reg exe

• Attempted Credential Dump From Registry via Reg exe

• BCDEdit Failure Recovery Modification

• BITS Job Persistence

• BITSAdmin Download File

• Batch File Write to System32

• CMD Echo Pipe - Escalation

• CMLUA Or CMSTPLUA UAC Bypass

• CertUtil Download With URLCache and Split Arguments

• CertUtil Download With VerifyCtl and Split Arguments

• CertUtil With Decode Argument

• Certutil exe certificate extraction

• Child Processes of Spoolsv exe

• Clop Common Exec Parameter

• Clop Ransomware Known Service Name

• Cobalt Strike Named Pipes

• Common Ransomware Extensions

• Common Ransomware Notes

• Conti Common Exec parameter

• Create Remote Thread into LSASS

• Create Service In Suspicious File Path

• Create local admin accounts using net exe

• Create or delete windows shares using net exe

• Creation of Shadow Copy

• Creation of Shadow Copy with wmic and powershell

• Creation of lsass Dump with Taskmgr

• Credential Dumping via Copy Command from Shadow Copy

• Credential Dumping via Symlink to Shadow Copy

• Credential Extraction indicative of FGDump and CacheDump with s option

• Credential Extraction indicative of FGDump and CacheDump with v option

• Credential Extraction indicative of Lazagne command line options

• Credential Extraction indicative of use of DSInternals credential conversion modules

• Credential Extraction indicative of use of DSInternals modules

• Credential Extraction indicative of use of Mimikatz modules

• Credential Extraction indicative of use of PowerSploit modules

• Credential Extraction native Microsoft debuggers peek into the kernel

• Credential Extraction native Microsoft debuggers via z command line option

• Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals

• DLLHost with no Command Line Arguments with Network

• DNS Exfiltration Using Nslookup App

• DSQuery Domain Discovery

• Delete ShadowCopy With PowerShell

• Deleting Of Net Users

• Deleting Shadow Copies

• Detect Activity Related to Pass the Hash Attacks

• Detect AzureHound Command-Line Arguments

• Detect AzureHound File Modifications

• Detect Baron Samedit CVE-2021-3156

• Detect Baron Samedit CVE-2021-3156 Segfault

• Detect Baron Samedit CVE-2021-3156 via OSQuery

• Detect Computer Changed with Anonymous Account

• Detect Credential Dumping through LSASS access

• Detect Dump LSASS Memory using comsvcs

• Detect Excessive Account Lockouts From Endpoint

• Detect Excessive User Account Lockouts

• Detect Exchange Web Shell

• Detect HTML Help Renamed

• Detect HTML Help Spawn Child Process

• Detect HTML Help URL in Command Line

• Detect HTML Help Using InfoTech Storage Handlers

• Detect Kerberoasting

• Detect MSHTA Url in Command Line

• Detect Mimikatz Using Loaded Images

• Detect New Local Admin account

• Detect Outlook exe writing a zip file

• Detect Pass the Hash

• Detect Path Interception By Creation Of program exe

• Detect Prohibited Applications Spawning cmd exe

• Detect Prohibited Applications Spawning cmd exe

• Detect PsExec With accepteula Flag

• Detect RClone Command-Line Usage

• Detect Rare Executables

• Detect Regasm Spawning a Process

• Detect Regasm with Network Connection

• Detect Regasm with no Command Line Arguments

• Detect Regsvcs Spawning a Process

• Detect Regsvcs with Network Connection

• Detect Regsvcs with No Command Line Arguments

• Detect Regsvr32 Application Control Bypass

• Detect Renamed 7-Zip

• Detect Renamed PSExec

• Detect Renamed RClone

• Detect Renamed WinRAR

• Detect Rundll32 Application Control Bypass - advpack

• Detect Rundll32 Application Control Bypass - setupapi

• Detect Rundll32 Application Control Bypass - syssetup

• Detect Rundll32 Inline HTA Execution

• Detect SharpHound Command-Line Arguments

• Detect SharpHound File Modifications

• Detect SharpHound Usage

• Detect Use of cmd exe to Launch Script Interpreters

• Detect mshta inline hta execution

• Detect mshta renamed

• Detect processes used for System Network Configuration Discovery

• Detection of tools built by NirSoft

• Disable Registry Tool

• Disable Show Hidden Files

• Disable Windows App Hotkeys

• Disable Windows Behavior Monitoring

• Disable Windows SmartScreen Protection

• Disabling CMD Application

• Disabling ControlPanel

• Disabling Firewall with Netsh

• Disabling FolderOptions Windows Feature

• Disabling Net User Account

• Disabling NoRun Windows App

• Disabling Remote User Account Control

• Disabling SystemRestore In Registry

• Disabling Task Manager

• Download Files Using Telegram

• Dump LSASS via comsvcs DLL

• Dump LSASS via procdump

• Dump LSASS via procdump Rename

• Enable RDP In Other Port Number

• Enumerate Users Local Group Using Telegram

• Eventvwr UAC Bypass

• Excel Spawning PowerShell

• Excel Spawning Windows Script Host

• Excessive Attempt To Disable Services

• Excessive Service Stop Attempt

• Excessive Usage Of Cacls App

• Excessive Usage Of Net App

• Excessive Usage Of Taskkill

• Excessive Usage of NSLOOKUP App

• Excessive number of taskhost processes

• Executables Or Script Creation In Suspicious Path

• Execution of File with Multiple Extensions

• Extract SAM from Registry

• File with Samsam Extension

• First Time Seen Child Process of Zoom

• First Time Seen Running Windows Service

• First time seen command line argument

• FodHelper UAC Bypass

• GPUpdate with no Command Line Arguments with Network

• Hide User Account From Sign-In Screen

• Hiding Files And Directories With Attrib exe

• High File Deletion Frequency

• High Process Termination Frequency

• ICACLS Grant Command

• Icacls Deny Command

• Illegal Access To User Content via PowerSploit modules

• Illegal Account Creation via PowerSploit modules

• Illegal Deletion of Logs via Mimikatz modules

• Illegal Enabling or Disabling of Accounts via DSInternals modules

• Illegal Management of Active Directory Elements and Policies via DSInternals modules

• Illegal Management of Computers and Active Directory Elements via PowerSploit modules

• Illegal Privilege Elevation and Persistence via PowerSploit modules

• Illegal Privilege Elevation via Mimikatz modules

• Illegal Service and Process Control via Mimikatz modules

• Illegal Service and Process Control via PowerSploit modules

• Kerberoasting spn request with RC4 encryption

• Known Services Killed by Ransomware

• MacOS - Re-opened Applications

• Mailsniper Invoke functions

• Malicious PowerShell Process - Connect To Internet With Hidden Window

• Malicious PowerShell Process - Encoded Command

• Malicious PowerShell Process - Execution Policy Bypass

• Malicious PowerShell Process With Obfuscation Techniques

• Malicious Powershell Executed As A Service

• Modification Of Wallpaper

• Modify ACL permission To Files Or Folder

• Monitor Registry Keys for Print Monitors

• More than usual number of LOLBAS applications in short time period

• Multiple Disabled Users Failing To Authenticate From Host Using Kerberos

• Multiple Invalid Users Failing To Authenticate From Host Using Kerberos

• Multiple Invalid Users Failing To Authenticate From Host Using NTLM

• Multiple Users Attempting To Authenticate Using Explicit Credentials

• Multiple Users Failing To Authenticate From Host Using Kerberos

• Multiple Users Failing To Authenticate From Host Using NTLM

• Multiple Users Failing To Authenticate From Process

• Multiple Users Remotely Failing To Authenticate From Host

• NLTest Domain Trust Discovery

• Nishang PowershellTCPOneLine

• Ntdsutil Export NTDS

• Office Application Spawn rundll32 process

• Office Document Creating Schedule Task

• Office Document Executing Macro Code

• Office Document Spawned Child Process To Download

• Office Product Spawning BITSAdmin

• Office Product Spawning CertUtil

• Office Product Spawning MSHTA

• Office Product Spawning Rundll32 with no DLL

• Office Product Spawning Wmic

• Overwriting Accessibility Binaries

• PowerShell Start-BitsTransfer

• Powershell Remote Thread To Known Windows Process

• Probing Access with Stolen Credentials via PowerSploit modules

• Process Creating LNK file in Suspicious Location

• Process Deleting Its Process File Path

• Process Execution via WMI

• Process Kill Base On File Path

• Processes Tapping Keyboard Events

• Processes launching netsh

• Ransomware Notes bulk creation

• Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules

• Reconnaissance and Access to Accounts and Groups via Mimikatz modules

• Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules

• Reconnaissance and Access to Computers and Domains via PowerSploit modules

• Reconnaissance and Access to Computers via Mimikatz modules

• Reconnaissance and Access to Operating System Elements via PowerSploit modules

• Reconnaissance and Access to Processes and Services via Mimikatz modules

• Reconnaissance and Access to Shared Resources via Mimikatz modules

• Reconnaissance and Access to Shared Resources via PowerSploit modules

• Reconnaissance of Access and Persistence Opportunities via PowerSploit modules

• Reconnaissance of Connectivity via PowerSploit modules

• Reconnaissance of Credential Stores and Services via Mimikatz modules

• Reconnaissance of Defensive Tools via PowerSploit modules

• Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules

• Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules

• Reg exe Manipulating Windows Services Registry Keys

• Registry Keys Used For Persistence

• Registry Keys Used For Privilege Escalation

• Registry Keys for Creating SHIM Databases

• Remote Desktop Process Running On System

• Remote Process Instantiation via WMI

• Resize ShadowStorage volume

• Revil Common Exec Parameter

• Revil Registry Entry

• RunDLL Loading DLL By Ordinal

• Rundll32 with no Command Line Arguments with Network

• Ryuk Test Files Detected

• Ryuk Wake on LAN Command

• SLUI RunAs Elevated

• SLUI Spawning a Process

• Samsam Test File Write

• Sc exe Manipulating Windows Services

• Schedule Task with HTTP Command Arguments

• Schedule Task with Rundll32 Command Trigger

• Scheduled Task Deleted Or Created via CMD

• Schtasks Run Task On Demand

• Schtasks scheduling job on remote system

• Schtasks used for forcing a reboot

• Script Execution via WMI

• SearchProtocolHost with no Command Line with Network

• SecretDumps Offline NTDS Dumping Tool

• Services Escalate Exe

• Set Default PowerShell Execution Policy To Unrestricted or Bypass

• Setting Credentials via DSInternals modules

• Setting Credentials via Mimikatz modules

• Setting Credentials via PowerSploit modules

• Shim Database File Creation

• Shim Database Installation With Suspicious Parameters

• Short Lived Windows Accounts

• Single Letter Process On Endpoint

• Spike in File Writes

• Sunburst Correlation DLL and Network Event

• Suspicious Curl Network Connection

• Suspicious DLLHost no Command Line Arguments

• Suspicious Driver Loaded Path

• Suspicious GPUpdate no Command Line Arguments

• Suspicious MSBuild Rename

• Suspicious MSBuild Spawn

• Suspicious PlistBuddy Usage

• Suspicious PlistBuddy Usage via OSquery

• Suspicious Process File Path

• Suspicious Reg exe Process

• Suspicious Regsvr32 Register Suspicious Path

• Suspicious Rundll32 Rename

• Suspicious Rundll32 StartW

• Suspicious Rundll32 dllregisterserver

• Suspicious Rundll32 no Command Line Arguments

• Suspicious SQLite3 LSQuarantine Behavior

• Suspicious Scheduled Task from Public Directory

• Suspicious SearchProtocolHost no Command Line Arguments

• Suspicious microsoft workflow compiler rename

• Suspicious microsoft workflow compiler usage

• Suspicious msbuild path

• Suspicious mshta child process

• Suspicious mshta spawn

• Suspicious wevtutil Usage

• Suspicious writes to windows Recycle Bin

• System Information Discovery Detection

• System Process Running from Unexpected Location

• System Processes Run From Unexpected Locations

• Trickbot Named Pipe

• USN Journal Deletion

• Unified Messaging Service Spawning a Process

• Unload Sysmon Filter Driver

• Unusually Long Command Line

• Unusually Long Command Line

• Unusually Long Command Line - MLTK

• W3WP Spawning Shell

• WBAdmin Delete System Backups

• WMI Permanent Event Subscription

• WMI Permanent Event Subscription - Sysmon

• WMI Temporary Event Subscription

• Wbemprox COM Object Execution

• Wermgr Process Connecting To IP Check Web Services

• Wermgr Process Create Executable File

• Wermgr Process Spawned CMD Or Powershell Process

• WinEvent Scheduled Task Created Within Public Path

• WinEvent Scheduled Task Created to Spawn Shell

• WinRM Spawning a Process

• Windows AdFind Exe

• Windows DisableAntiSpyware Registry

• Windows Event Log Cleared

• Windows Security Account Manager Stopped

• Winword Spawning Cmd

• Winword Spawning PowerShell

• Winword Spawning Windows Script Host

• Write Executable in SMB Share

• XMRIG Driver Loaded

Network

details

• DNS Query Length Outliers - MLTK

• DNS Query Length With High Standard Deviation

• Detect ARP Poisoning

• Detect IPv6 Network Infrastructure Threats

• Detect Large Outbound ICMP Packets

• Detect Outbound SMB Traffic

• Detect Port Security Violation

• Detect Rogue DHCP Server

• Detect SNICat SNI Exfiltration

• Detect Software Download To Network Device

• Detect Traffic Mirroring

• Detect Unauthorized Assets by MAC address

• Detect Windows DNS SIGRed via Splunk Stream

• Detect Windows DNS SIGRed via Zeek

• Detect Zerologon via Zeek

• Detect hosts connecting to dynamic domain providers

• Excessive DNS Failures

• Hosts receiving high volume of network traffic from email server

• Large Volume of DNS ANY Queries

• Multiple Archive Files Http Post Traffic

• Plain HTTP POST Exfiltrated Data

• Prohibited Network Traffic Allowed

• Protocol or Port Mismatch

• Protocols passing authentication in cleartext

• Remote Desktop Network Bruteforce

• Remote Desktop Network Traffic

• SMB Traffic Spike

• SMB Traffic Spike - MLTK

• TOR Traffic

• Unusually Long Content-Type Length

Application

details

• Detect New Login Attempts to Routers

• Email Attachments With Lots Of Spaces

• Email files written outside of the Outlook directory

• Email servers sending high volume traffic to hosts

• Monitor Email For Brand Abuse

• Multiple Okta Users With Invalid Credentials From The Same IP

• No Windows Updates in a time frame

• Okta Account Lockout Events

• Okta Failed SSO Attempts

• Okta User Logins From Multiple Cities

• Phishing Email Detection by Machine Learning Method - SSA

• Suspicious Email Attachment Extensions

• Suspicious Java Classes

• Web Servers Executing Suspicious Processes

Web

details

• Detect F5 TMUI RCE CVE-2020-5902

• Detect attackers scanning for vulnerable JBoss servers

• Detect malicious requests to exploit JBoss servers

• Monitor Web Traffic For Brand Abuse

• SQL Injection with Long URLs

• Supernova Webshell

AWS Cloud Provisioning From Previously Unseen City

This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1535
• Last Updated: 2018-03-16

details

Search

`cloudtrail` (eventName=Run* OR eventName=Create*) 
| iplocation sourceIPAddress 
| search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) 
| iplocation sourceIPAddress 
| search City=* 
| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country 
| inputlookup append=t previously_seen_provisioning_activity_src.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country 
| outputlookup previously_seen_provisioning_activity_src.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by City 
| eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newCity=1 
| table City] 
| spath output=user userIdentity.arn 
| rename sourceIPAddress as src_ip 
| table _time, user, src_ip, City, eventName, errorCode 
| `aws_cloud_provisioning_from_previously_unseen_city_filter`

Associated Analytic Story

• AWS Suspicious Provisioning Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources.

Required field

• _time

• eventName

• sourceIPAddress

ATT&CK

ID	Technique	Tactic
T1535	Unused/Unsupported Cloud Regions	Defense Evasion

Kill Chain Phase

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
This search will fire any time a new city is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Reference

Test Dataset

version: 1

---

AWS Cloud Provisioning From Previously Unseen Country

This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1535
• Last Updated: 2018-03-16

details

Search

`cloudtrail` (eventName=Run* OR eventName=Create*) 
| iplocation sourceIPAddress 
| search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) 
| iplocation sourceIPAddress 
| search Country=* 
| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country 
| inputlookup append=t previously_seen_provisioning_activity_src.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country 
| outputlookup previously_seen_provisioning_activity_src.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by Country 
| eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newCountry=1 
| table Country] 
| spath output=user userIdentity.arn 
| rename sourceIPAddress as src_ip 
| table _time, user, src_ip, Country, eventName, errorCode 
| `aws_cloud_provisioning_from_previously_unseen_country_filter`

Associated Analytic Story

• AWS Suspicious Provisioning Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources.

Required field

• _time

• eventName

• sourceIPAddress

ATT&CK

ID	Technique	Tactic
T1535	Unused/Unsupported Cloud Regions	Defense Evasion

Kill Chain Phase

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
This search will fire any time a new country is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Reference

Test Dataset

version: 1

---

AWS Cloud Provisioning From Previously Unseen IP Address

This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2018-03-16

details

Search

`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) 
| iplocation sourceIPAddress 
| search Country=* 
| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country 
| inputlookup append=t previously_seen_provisioning_activity_src.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country 
| outputlookup previously_seen_provisioning_activity_src.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress 
| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newIP=1 
| table sourceIPAddress] 
| spath output=user userIdentity.arn 
| rename sourceIPAddress as src_ip 
| table _time, user, src_ip, eventName, errorCode 
| `aws_cloud_provisioning_from_previously_unseen_ip_address_filter`

Associated Analytic Story

• AWS Suspicious Provisioning Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources.

Required field

• _time

• eventName

• sourceIPAddress

Kill Chain Phase

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
This search will fire any time a new IP address is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Reference

Test Dataset

version: 1

---

AWS Cloud Provisioning From Previously Unseen Region

This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1535
• Last Updated: 2018-03-16

details

Search

`cloudtrail` (eventName=Run* OR eventName=Create*) 
| iplocation sourceIPAddress 
| search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) 
| iplocation sourceIPAddress 
| search Region=* 
| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country 
| inputlookup append=t previously_seen_provisioning_activity_src.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country 
| outputlookup previously_seen_provisioning_activity_src.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by Region 
| eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newRegion=1 
| table Region] 
| spath output=user userIdentity.arn 
| rename sourceIPAddress as src_ip 
| table _time, user, src_ip, Region, eventName, errorCode 
| `aws_cloud_provisioning_from_previously_unseen_region_filter`

Associated Analytic Story

• AWS Suspicious Provisioning Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources.

Required field

• _time

• eventName

• sourceIPAddress

ATT&CK

ID	Technique	Tactic
T1535	Unused/Unsupported Cloud Regions	Defense Evasion

Kill Chain Phase

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
This search will fire any time a new region is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Reference

Test Dataset

version: 1

---

AWS Create Policy Version to allow all resources

This search looks for CloudTrail events where a user created a policy version that allows them to access any resource in their account

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2021-02-22

details

Search

`cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com errorCode = success 
| spath input=requestParameters.policyDocument output=key_policy_statements path=Statement{} 
| mvexpand key_policy_statements 
| spath input=key_policy_statements output=key_policy_action_1 path=Action 
| search key_policy_action_1 = "*" 
| stats count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements) as policy_added by eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
|`aws_create_policy_version_to_allow_all_resources_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.userName

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives, it is possible that an AWS admin has legitimately created a policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources

Reference

• https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

• https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/aws_cloudtrail_events.json

version: 2

---

AWS CreateAccessKey

This search looks for CloudTrail events where a user A who has already permission to create access keys, makes an API call to create access keys for another user B. Attackers have been know to use this technique for Privilege Escalation in case new victim(user B) has more permissions than old victim(user B)

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1136.003
• Last Updated: 2021-03-02

details

Search

`cloudtrail` eventName = CreateAccessKey userAgent !=console.amazonaws.com errorCode = success
| search userName!=requestParameters.userName  
|  stats count min(_time) as firstTime max(_time) as lastTime  by requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
|`aws_createaccesskey_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.userName

ATT&CK

ID	Technique	Tactic
T1136.003	Cloud Account	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.

Reference

• https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

• https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/aws_cloudtrail_events.json

version: 1

---

AWS CreateLoginProfile

This search looks for CloudTrail events where a user A(victim A) creates a login profile for user B, followed by a AWS Console login event from user B from the same src_ip as user B. This correlated event can be indicative of privilege escalation since both events happened from the same src_ip

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1136.003
• Last Updated: 2021-03-02

details

Search

`cloudtrail` eventName = CreateLoginProfile 
| rename requestParameters.userName as new_login_profile 
| table src_ip eventName new_login_profile userName 
| join new_login_profile src_ip [
| search `cloudtrail` eventName = ConsoleLogin 
| rename userName as new_login_profile 
| stats count values(eventName) min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn new_login_profile src_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`] 
| `aws_createloginprofile_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.userName

ATT&CK

ID	Technique	Tactic
T1136.003	Cloud Account	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives, it is possible that an AWS admin has legitimately created a login profile for another user.

Reference

• https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

• https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createloginprofile/aws_cloudtrail_events.json

version: 1

---

AWS Cross Account Activity From Previously Unseen Account

This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. This search is deprecated and have been translated to use the latest Authentication Datamodel.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Authentication
• ATT&CK:
• Last Updated: 2020-05-28

details

Search

| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role 
| `drop_dm_object_name(Authentication)` 
| rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" 
| where vendor_account != dest_account 
| rename vendor_account as requestingAccountId dest_account as requestedAccountId 
| lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime 
| eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") 
|  where status = "New Cross Account Activity" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `aws_cross_account_activity_from_previously_unseen_account_filter`

Associated Analytic Story

• Suspicious Cloud Authentication Activities

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen AWS Cross Account Activity - Initial to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search Previously Seen AWS Cross Account Activity - Update to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the aws_cross_account_activity_from_previously_unseen_account_filter macro.

Required field

• _time

• Authentication.signature

• Authentication.vendor_account

• Authentication.user

• Authentication.user_role

• Authentication.src

Kill Chain Phase

• Actions on Objectives

Known False Positives

Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

AWS Detect Users creating keys with encrypt policy without MFA

This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1486
• Last Updated: 2021-01-11

details

Search

`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy 
| spath input=requestParameters.policy output=key_policy_statements path=Statement{} 
| mvexpand key_policy_statements 
| spath input=key_policy_statements output=key_policy_action_1 path=Action 
| spath input=key_policy_statements output=key_policy_action_2 path=Action{} 
| eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) 
| spath input=key_policy_statements output=key_policy_principal path=Principal.AWS 
| search key_policy_action="kms:Encrypt" AND key_policy_principal="*" 
| stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter`

Associated Analytic Story

• Ransomware Cloud

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs

Required field

• _time

• eventName

• eventSource

• eventID

• awsRegion

• requestParameters.policy

• userIdentity.principalId

ATT&CK

ID	Technique	Tactic
T1486	Data Encrypted for Impact	Impact

Kill Chain Phase

Known False Positives

unknown

Reference

• https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/

• https://github.com/d1vious/git-wild-hunt

• https://www.youtube.com/watch?v=PgzNib37g0M

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json

version: 1

---

AWS Detect Users with KMS keys performing encryption S3

This search provides detection of users with KMS keys performing encryption specifically against S3 buckets.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1486
• Last Updated: 2021-01-11

details

Search

`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" 
| rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file 
| stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`aws_detect_users_with_kms_keys_performing_encryption_s3_filter`

Associated Analytic Story

• Ransomware Cloud

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs

Required field

• _time

• eventName

• requestParameters.x-amz-server-side-encryption

• requestParameters.bucketName

• requestParameters.x-amz-copy-source

• requestParameters.key

• userAgent

• region

ATT&CK

ID	Technique	Tactic
T1486	Data Encrypted for Impact	Impact

Kill Chain Phase

Known False Positives

bucket with S3 encryption

Reference

• https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/

• https://github.com/d1vious/git-wild-hunt

• https://www.youtube.com/watch?v=PgzNib37g0M

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json

version: 1

---

AWS EKS Kubernetes cluster sensitive object access

This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-06-23

details

Search

`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1  
|table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason 
|dedup user.username user.groups{} 
|`aws_eks_kubernetes_cluster_sensitive_object_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs.

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.

Reference

Test Dataset

version: 1

---

AWS Excessive Security Scanning

This search looks for CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1526
• Last Updated: 2021-04-13

details

Search

`cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get*  
| stats dc(eventName) as dc_events min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName values(src) as src values(userAgent) as userAgent by user userIdentity.arn 
| where dc_events > 50 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
|`aws_excessive_security_scanning_filter`

Associated Analytic Story

• AWS User Monitoring

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.

Required field

• _time

• eventName

• src

• userAgent

• user

• userIdentity.arn

ATT&CK

ID	Technique	Tactic
T1526	Cloud Service Discovery	Discovery

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives.

Reference

• https://github.com/aquasecurity/cloudsploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/aws_security_scanner/aws_security_scanner.json

version: 1

---

AWS IAM AccessDenied Discovery Events

The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
• Datamodel:
• ATT&CK: T1580
• Last Updated: 2021-04-05

details

Search

`cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) 
| bucket _time span=1h 
| stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources values(userIdentity.arn) by src_ip, userIdentity.arn, _time 
| where failures >= 5 and methods >= 1 and sources >= 1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_accessdenied_discovery_events_filter`

Associated Analytic Story

• Suspicious Cloud User Activities

How To Implement

The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.

Required field

• _time

• eventName

• eventSource

• userAgent

• errorCode

• userIdentity.type

ATT&CK

ID	Technique	Tactic
T1580	Cloud Infrastructure Discovery	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives.

Reference

• https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/aws_iam_accessdenied_discovery_events.json

version: 1

---

AWS IAM Assume Role Policy Brute Force

The following detection identifies any malformed policy document exceptions with a status of failure. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS. Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
• Datamodel:
• ATT&CK: T1580, T1110
• Last Updated: 2021-04-01

details

Search

`cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn 
| where count >= 2 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_assume_role_policy_brute_force_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs. Set the where count greater than a value to identify suspicious activity in your environment.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.policyName

ATT&CK

ID	Technique	Tactic
T1580	Cloud Infrastructure Discovery	Discovery
T1110	Brute Force	Credential Access

Kill Chain Phase

• Reconnaissance

Known False Positives

This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users.

Reference

• https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities

• https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/

• https://www.elastic.co/guide/en/security/current/aws-iam-brute-force-of-assume-role-policy.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/aws_iam_assume_role_policy_brute_force.json

version: 1

---

AWS IAM Delete Policy

The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
• Datamodel:
• ATT&CK: T1098
• Last Updated: 2021-04-01

details

Search

`cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com) 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policyArn by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId userIdentity.arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_delete_policy_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.policyArn

ATT&CK

ID	Technique	Tactic
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.

Reference

• https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html

• https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/aws_iam_delete_policy.json

version: 1

---

AWS IAM Failure Group Deletion

This detection identifies failure attempts to delete groups. We want to identify when a group is attempting to be deleted, but either access is denied, there is a conflict or there is no group. This is indicative of administrators performing an action, but also could be suspicious behavior occurring. Review parallel IAM events - recently added users, new groups and so forth.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
• Datamodel:
• ATT&CK: T1098
• Last Updated: 2021-04-01

details

Search

`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com) 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_name by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_failure_group_deletion_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.groupName

ATT&CK

ID	Technique	Tactic
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).

Reference

• https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html

• https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/aws_iam_failure_group_deletion.json

version: 1

---

AWS IAM Successful Group Deletion

The following query uses IAM events to track the success of a group being deleted on AWS. This is typically not indicative of malicious behavior, but a precurser to additional events thay may unfold. Review parallel IAM events - recently added users, new groups and so forth. Inversely, review failed attempts in a similar manner.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
• Datamodel:
• ATT&CK: T1069.003, T1098
• Last Updated: 2021-03-31

details

Search

`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com) 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) by src eventName eventSource errorCode user_agent awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_successful_group_deletion_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.groupName

ATT&CK

ID	Technique	Tactic
T1069.003	Cloud Groups	Discovery
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).

Reference

• https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html

• https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/aws_iam_successful_group_deletion.json

version: 1

---

AWS Network Access Control List Created with All Open Ports

The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1562.007
• Last Updated: 2021-01-11

details

Search

`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 
| append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 
| eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' 
| where port_range>1024] 
| fillnull 
| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `aws_network_access_control_list_created_with_all_open_ports_filter`

Associated Analytic Story

• AWS Network ACL Activity

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs.

Required field

• _time

• eventName

• requestParameters.ruleAction

• requestParameters.egress

• requestParameters.aclProtocol

• requestParameters.portRange.to

• requestParameters.portRange.from

• requestParameters.cidrBlock

• userName

• userIdentity.principalId

• userAgent

ATT&CK

ID	Technique	Tactic
T1562.007	Disable or Modify Cloud Firewall	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json

version: 2

---

AWS Network Access Control List Deleted

Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1562.007
• Last Updated: 2021-01-12

details

Search

`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false 
| fillnull 
| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `aws_network_access_control_list_deleted_filter`

Associated Analytic Story

• AWS Network ACL Activity

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.

Required field

• _time

• eventName

• requestParameters.egress

• userName

• userIdentity.principalId

• src

• userAgent

ATT&CK

ID	Technique	Tactic
T1562.007	Disable or Modify Cloud Firewall	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's possible that a user has legitimately deleted a network ACL.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json

version: 2

---

AWS SAML Access by Provider User and Principal

This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2021-01-26

details

Search

`cloudtrail` eventName=Assumerolewithsaml 
| stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`aws_saml_access_by_provider_user_and_principal_filter`

Associated Analytic Story

• Cloud Federated Credential Abuse

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs

Required field

• _time

• eventName

• requestParameters.principalArn

• requestParameters.roleArn

• requestParameters.roleSessionName

• recipientAccountId

• responseElements.issuer

• sourceIPAddress

• userAgent

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks.

Reference

• https://us-cert.cisa.gov/ncas/alerts/aa21-008a

• https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html

• https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

• https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json

version: 1

---

AWS SAML Update identity provider

This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2021-01-26

details

Search

`cloudtrail` eventName=UpdateSAMLProvider 
| stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`aws_saml_update_identity_provider_filter`

Associated Analytic Story

• Cloud Federated Credential Abuse

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.

Required field

• _time

• eventName

• eventType

• requestParameters.sAMLProviderArn

• userIdentity.sessionContext.sessionIssuer.arn

• sourceIPAddress

• userIdentity.accessKeyId

• userIdentity.principalId

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored.

Reference

• https://us-cert.cisa.gov/ncas/alerts/aa21-008a

• https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html

• https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

• https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json

version: 1

---

AWS SetDefaultPolicyVersion

This search looks for CloudTrail events where a user has set a default policy versions. Attackers have been know to use this technique for Privilege Escalation in case the previous versions of the policy had permissions to access more resources than the current version of the policy

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2021-03-02

details

Search

`cloudtrail` eventName=SetDefaultPolicyVersion eventSource = iam.amazonaws.com 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policy_arn by src requestParameters.versionId eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_setdefaultpolicyversion_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.userName

• eventSource

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives, it is possible that an AWS admin has legitimately set a default policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources

Reference

• https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

• https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_setdefaultpolicyversion/aws_cloudtrail_events.json

version: 1

---

AWS UpdateLoginProfile

This search looks for CloudTrail events where a user A who has already permission to update login profile, makes an API call to update login profile for another user B . Attackers have been know to use this technique for Privilege Escalation in case new victim(user B) has more permissions than old victim(user B)

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1136.003
• Last Updated: 2021-03-02

details

Search

`cloudtrail` eventName = UpdateLoginProfile userAgent !=console.amazonaws.com errorCode = success
| search userName!=requestParameters.userName  
|  stats count min(_time) as firstTime max(_time) as lastTime  by requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userName user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
|`aws_updateloginprofile_filter`

Associated Analytic Story

• AWS IAM Privilege Escalation

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.

Required field

• _time

• eventName

• userAgent

• errorCode

• requestParameters.userName

ATT&CK

ID	Technique	Tactic
T1136.003	Cloud Account	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.

Reference

• https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

• https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/aws_cloudtrail_events.json

version: 1

---

Abnormally High AWS Instances Launched by User

This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` eventName=RunInstances errorCode=success 
| bucket span=10m _time 
| stats count AS instances_launched by _time userName 
| eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev 
| eval threshold_value = 4 
| eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) 
| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") 
| eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) 
| table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev 
| `abnormally_high_aws_instances_launched_by_user_filter`

Associated Analytic Story

• AWS Cryptomining

• Suspicious AWS EC2 Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment.

Required field

• _time

• eventName

• errorCode

• userName

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.

Reference

Test Dataset

version: 2

---

Abnormally High AWS Instances Launched by User - MLTK

This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` 
| bucket span=10m _time  
| stats count as instances_launched by _time src_user  
| apply ec2_excessive_runinstances_v1  
| rename "IsOutlier(instances_launched)" as isOutlier  
| where isOutlier=1

Associated Analytic Story

• AWS Cryptomining

• Suspicious AWS EC2 Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment.

Required field

• _time

• eventName

• errorCode

• src_user

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.

Reference

Test Dataset

version: 2

---

Abnormally High AWS Instances Terminated by User

This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` eventName=TerminateInstances errorCode=success 
| bucket span=10m _time 
| stats count AS instances_terminated by _time userName 
| eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev 
| eval threshold_value = 4 
| eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) 
| search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")
| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) 
|table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev 
| `abnormally_high_aws_instances_terminated_by_user_filter`

Associated Analytic Story

• Suspicious AWS EC2 Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.

Required field

• _time

• eventName

• errorCode

• userName

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user.

Reference

Test Dataset

version: 2

---

Abnormally High AWS Instances Terminated by User - MLTK

This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` 
| bucket span=10m _time  
| stats count as instances_terminated by _time src_user  
| apply ec2_excessive_terminateinstances_v1  
| rename "IsOutlier(instances_terminated)" as isOutlier  
| where isOutlier=1

Associated Analytic Story

• Suspicious AWS EC2 Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment.

Required field

• _time

• eventName

• errorCode

• src_user

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.

Reference

Test Dataset

version: 2

---

Abnormally High Number Of Cloud Infrastructure API Calls

This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078.004
• Last Updated: 2020-09-07

details

Search

| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] 
| where cardinality >=16 
| apply cloud_excessive_api_calls_v1 threshold=0.005 
| rename "IsOutlier(api_calls)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| where api_calls > expected_upper_threshold 
| eval distance_from_threshold = api_calls - expected_upper_threshold 
| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold 
| `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`

Associated Analytic Story

• Suspicious Cloud User Activities

How To Implement

You must be ingesting your cloud infrastructure logs. You also must run the baseline search Baseline Of Cloud Infrastructure API Calls Per User to create the probability density function.

Required field

• _time

• All_Changes.command

• All_Changes.user

• All_Changes.status

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Abnormally High Number Of Cloud Instances Destroyed

This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078.004
• Last Updated: 2020-08-21

details

Search

| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] 
| where cardinality >=16 
| apply cloud_excessive_instances_destroyed_v1 threshold=0.005 
| rename "IsOutlier(instances_destroyed)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| eval distance_from_threshold = instances_destroyed - expected_upper_threshold 
| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id 
| `abnormally_high_number_of_cloud_instances_destroyed_filter`

Associated Analytic Story

• Suspicious Cloud Instance Activities

How To Implement

You must be ingesting your cloud infrastructure logs. You also must run the baseline search Baseline Of Cloud Instances Destroyed to create the probability density function.

Required field

• _time

• All_Changes.object_id

• All_Changes.action

• All_Changes.status

• All_Changes.object_category

• All_Changes.user

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.

Reference

Test Dataset

version: 1

---

Abnormally High Number Of Cloud Instances Launched

This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078.004
• Last Updated: 2020-08-21

details

Search

| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] 
| where cardinality >=16 
| apply cloud_excessive_instances_created_v1 threshold=0.005 
| rename "IsOutlier(instances_launched)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| eval distance_from_threshold = instances_launched - expected_upper_threshold 
| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id 
| `abnormally_high_number_of_cloud_instances_launched_filter`

Associated Analytic Story

• Cloud Cryptomining

• Suspicious Cloud Instance Activities

How To Implement

You must be ingesting your cloud infrastructure logs. You also must run the baseline search Baseline Of Cloud Instances Launched to create the probability density function.

Required field

• _time

• All_Changes.object_id

• All_Changes.action

• All_Changes.status

• All_Changes.object_category

• All_Changes.user

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.

Reference

Test Dataset

version: 2

---

Abnormally High Number Of Cloud Security Group API Calls

This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078.004
• Last Updated: 2020-09-07

details

Search

| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] 
| where cardinality >=16 
| apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 
| rename "IsOutlier(security_group_api_calls)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| where security_group_api_calls > expected_upper_threshold 
| eval distance_from_threshold = security_group_api_calls - expected_upper_threshold 
| table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold 
| `abnormally_high_number_of_cloud_security_group_api_calls_filter`

Associated Analytic Story

• Suspicious Cloud User Activities

How To Implement

You must be ingesting your cloud infrastructure logs. You also must run the baseline search Baseline Of Cloud Security Group API Calls Per User to create the probability density function model.

Required field

• _time

• All_Changes.command

• All_Changes.object_category

• All_Changes.status

• All_Changes.user

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Access LSASS Memory for Dump Creation

Detect memory dumping of the LSASS process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1003.001
• Last Updated: 2019-12-06

details

Search

`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `access_lsass_memory_for_dump_creation_filter` 

Associated Analytic Story

• Credential Dumping

How To Implement

This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named sysmon. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

Required field

• _time

• EventCode

• TargetImage

• CallTrace

• Computer

• TargetProcessId

• SourceImage

• SourceProcessId

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.

Reference

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log

version: 2

---

Account Discovery With Net App

this search is to detect a potential account discovery series of command used by several malware or attack to recon the target machine. This technique is also seen in some note worthy malware like trickbot where it runs a cmd process, or even drop its module that will execute the said series of net command. This series of command are good correlation search and indicator of attacker recon if seen in the machines within a none technical user or department (HR, finance, ceo and etc) network.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1087.002
• Last Updated: 2021-05-03

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND (Processes.process="*user*" OR  Processes.process="*config*" OR Processes.process="*view /all*") by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name 
| where count >=5 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `account_discovery_with_net_app_filter`

Associated Analytic Story

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.

Required field

• _time

• Processes.parent_process_name

• Processes.parent_process_id

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.process_guid

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1087.002	Domain Account	Discovery

Kill Chain Phase

• Exploitation

Known False Positives

admin or power user may used this series of command.

Reference

• https://labs.vipre.com/trickbot-and-its-modules/

• https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log

version: 1

---

Allow Inbound Traffic By Firewall Rule Registry

This analytic detects a potential suspicious modification of firewall rule registry allowing inbound traffic in specific port with public profile. This technique was seen in some attacker want to have a remote access to a machine by allowing the traffic in firewall rule.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1021.001
• Last Updated: 2021-05-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.registry_value_name = "*
|Action=Allow
|*" Registry.registry_value_name = "*
|Dir=In
|*" Registry.registry_value_name = "*
|Profile=Public
|*" Registry.registry_value_name = "*
|LPort=*" by Registry.registry_path Registry.registry_key_name Registry.user Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `allow_inbound_traffic_by_firewall_rule_registry_filter`

Associated Analytic Story

• Prohibited Traffic Allowed or Protocol Mismatch

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_path

• Registry.registry_value_name

• Registry.registry_key_name

• Registry.dest

• Registry.user

ATT&CK

ID	Technique	Tactic
T1021.001	Remote Desktop Protocol	Lateral Movement

Kill Chain Phase

• Exploitation

Known False Positives

network admin may add/remove/modify public inbound firewall rule that may cause this rule to be triggered.

Reference

• https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log

version: 1

---

Allow Inbound Traffic In Firewall Rule

This search is to detect suspicious powershell command to allow inbound traffic in specific local port with public profile. This technique was seen in some attacker want to have a remote access to a machine by allowing the traffic in firewall rule.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1021.001
• Last Updated: 2021-05-19

details

Search

`powershell` EventCode=4104 Message = "*firewall*" Message = "*Public*" Message = "*Inbound*" Message = "*Allow*"  Message = "*-LocalPort*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `allow_inbound_traffic_in_firewall_rule_filter`

Associated Analytic Story

• Prohibited Traffic Allowed or Protocol Mismatch

How To Implement

To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event.

Required field

• _time

• EventCode

• Message

• ComputerName

• User

ATT&CK

ID	Technique	Tactic
T1021.001	Remote Desktop Protocol	Lateral Movement

Kill Chain Phase

• Exploitation

Known False Positives

administrator may allow inbound traffic in certain network or machine.

Reference

• https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-powershell.log

version: 1

---

Amazon EKS Kubernetes Pod scan detection

This search provides detection information on unauthenticated requests against Kubernetes' Pods API

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1526
• Last Updated: 2020-04-15

details

Search

`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" 
| rename source as cluster_name sourceIPs{} as src_ip 
| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `amazon_eks_kubernetes_pod_scan_detection_filter` 

Associated Analytic Story

• Kubernetes Scanning Activity

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the kubernetes_pods_aws_scan_fingerprint_detection macro to filter out the false positives.

Required field

• _time

• user.username

• verb

• objectRef.resource

• requestURI

• source

• sourceIPs{}

• responseStatus.reason

• responseStatus.code

• userAgent

• src_ip

• user.groups{}

ATT&CK

ID	Technique	Tactic
T1526	Cloud Service Discovery	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context.

Reference

Test Dataset

version: 1

---

Amazon EKS Kubernetes cluster scan detection

This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1526
• Last Updated: 2020-04-15

details

Search

`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" 
| rename sourceIPs{} as src_ip 
| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
|`amazon_eks_kubernetes_cluster_scan_detection_filter` 

Associated Analytic Story

• Kubernetes Scanning Activity

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs.

Required field

• _time

• user.username

• userAgent

• sourceIPs{}

• responseStatus.reason

• source

• responseStatus.code

• verb

• requestURI

• src_ip

• user.groups{}

ATT&CK

ID	Technique	Tactic
T1526	Cloud Service Discovery	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context.

Reference

Test Dataset

version: 1

---

Anomalous usage of 7zip

The following detection identifies a 7z.exe spawned from Rundll32.exe or Dllhost.exe. It is assumed that the adversary has brought in 7z.exe and 7z.dll. It has been observed where an adversary will rename 7z.exe. Additional coverage may be required to identify the behavior of renamed instances of 7z.exe. During triage, identify the source of injection into Rundll32.exe or Dllhost.exe. Capture any files written to disk and analyze as needed. Review parallel processes for additional behaviors. Typically, archiving files will result in exfiltration.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1560.001
• Last Updated: 2021-04-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `anomalous_usage_of_7zip_filter`

Associated Analytic Story

• Cobalt Strike

• NOBELIUM Group

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1560.001	Archive via Utility	Collection

Kill Chain Phase

• Actions on Objective

Known False Positives

False positives should be limited as this behavior is not normal for rundll32.exe or dllhost.exe to spawn and run 7zip.

Reference

• https://attack.mitre.org/techniques/T1560/001/

• https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/

• https://thedfirreport.com/2021/01/31/bazar-no-ryuk/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log

version: 1

---

Any Powershell DownloadFile

The following analytic identifies the use of PowerShell downloading a file using DownloadFile method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001
• Last Updated: 2021-03-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe OR Processes.process_name=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe) Processes.process=*DownloadFile* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `any_powershell_downloadfile_filter`

Associated Analytic Story

• Malicious PowerShell

• Ingress Tool Transfer

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution

Kill Chain Phase

• Exploitation

Known False Positives

False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.

Reference

• https://docs.microsoft.com/en-us/dotnet/api/system.net.webclient.downloadfile?view=net-5.0

• https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log

version: 1

---

Any Powershell DownloadString

The following analytic identifies the use of PowerShell downloading a file using DownloadString method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001
• Last Updated: 2021-03-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe OR Processes.process_name=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe Processes.process=*.DownloadString*  by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `any_powershell_downloadstring_filter`

Associated Analytic Story

• Malicious PowerShell

• HAFNIUM Group

• Ingress Tool Transfer

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution

Kill Chain Phase

• Exploitation

Known False Positives

False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.

Reference

• https://docs.microsoft.com/en-us/dotnet/api/system.net.webclient.downloadstring?view=net-5.0

• https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log

version: 1

---

Applying Stolen Credentials via Mimikatz modules

This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1055, T1068, T1078, T1098, T1134, T1543, T1547, T1548, T1554, T1556, T1558
• Last Updated: 2020-11-03

details

Search

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)kerberos::ptt/)=true OR match_regex(cmd_line, /(?i)kerberos::golden/)=true OR match_regex(cmd_line, /(?i)kerberos::silver/)=true OR match_regex(cmd_line, /(?i)misc::skeleton/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation
T1068	Exploitation for Privilege Escalation	Privilege Escalation
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence
T1134	Access Token Manipulation	Defense Evasion, Privilege Escalation
T1543	Create or Modify System Process	Persistence, Privilege Escalation
T1547	Boot or Logon Autostart Execution	Persistence, Privilege Escalation
T1548	Abuse Elevation Control Mechanism	Defense Evasion, Privilege Escalation
T1554	Compromise Client Software Binary	Persistence
T1556	Modify Authentication Process	Credential Access, Defense Evasion, Persistence
T1558	Steal or Forge Kerberos Tickets	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

• https://adsecurity.org/?p=1275

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/applying_stolen_credentials/logAllMimikatzModules.log

version: 1

---

Applying Stolen Credentials via PowerSploit modules

Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1055, T1068, T1078, T1098, T1134, T1543, T1547, T1548, T1554, T1555, T1558
• Last Updated: 2020-11-03

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-CredentialInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-TokenManipulation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserImpersonation/)=true OR match_regex(cmd_line, /(?i)Get-System/)=true OR match_regex(cmd_line, /(?i)Invoke-RevertToSelf/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation
T1068	Exploitation for Privilege Escalation	Privilege Escalation
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence
T1134	Access Token Manipulation	Defense Evasion, Privilege Escalation
T1543	Create or Modify System Process	Persistence, Privilege Escalation
T1547	Boot or Logon Autostart Execution	Persistence, Privilege Escalation
T1548	Abuse Elevation Control Mechanism	Defense Evasion, Privilege Escalation
T1554	Compromise Client Software Binary	Persistence
T1555	Credentials from Password Stores	Credential Access
T1558	Steal or Forge Kerberos Tickets	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/applying_stolen_credentials/logAllPowerSploitModulesWithOldNames.log

version: 1

---

Assessment of Credential Strength via DSInternals modules

This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1078, T1098, T1087, T1201, T1552, T1555
• Last Updated: 2020-11-03

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence
T1087	Account Discovery	Discovery
T1201	Password Policy Discovery	Discovery
T1552	Unsecured Credentials	Credential Access
T1555	Credentials from Password Stores	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/MichaelGrafnetter/DSInternals

Test Dataset

version: 1

---

Attempt To Add Certificate To Untrusted Store

Attempt To Add Certificate To Untrusted Store

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1553.004
• Last Updated: 2020-11-03

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `attempt_to_add_certificate_to_untrusted_store_filter`

Associated Analytic Story

• Disabling Security Tools

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.parent_process

• Processes.user

ATT&CK

ID	Technique	Tactic
T1553.004	Install Root Certificate	Defense Evasion

Kill Chain Phase

• Installation

• Actions on Objectives

Known False Positives

There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log

version: 6

---

Attempt To Stop Security Service

This search looks for attempts to stop security-related services on the endpoint.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR  Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
|lookup security_services_lookup service as process OUTPUTNEW category, description 
| search category=security 
| `attempt_to_stop_security_service_filter`

Associated Analytic Story

• Disabling Security Tools

• Trickbot

How To Implement

You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, security_services.csv, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in $SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service.,

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Installation

• Actions on Objectives

Known False Positives

None identified. Attempts to disable security-related services should be identified and understood.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log

version: 3

---

Attempted Credential Dump From Registry via Reg exe

Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.002
• Last Updated: 2019-12-02

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest Processes.process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `attempted_credential_dump_from_registry_via_reg_exe_filter`

Associated Analytic Story

• Credential Dumping

• DarkSide Ransomware

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1003.002	Security Account Manager	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log

version: 4

---

Attempted Credential Dump From Registry via Reg exe

Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-6-04

details

Search

 
| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) 
| where process_name="cmd.exe" OR process_name="reg.exe" 
| where cmd_line != null  AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body=create_map(["cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events(); 

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting windows endpoint data that tracks process activity, including parent-child relationships from your endpoints.

Required field

• process_name

• _time

• dest_device_id

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml

Test Dataset

version: 1

---

BCDEdit Failure Recovery Modification

This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1490
• Last Updated: 2020-12-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `bcdedit_failure_recovery_modification_filter`

Associated Analytic Story

• Ryuk Ransomware

• Ransomware

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1490	Inhibit System Recovery	Impact

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators may modify the boot configuration.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log

version: 1

---

BITS Job Persistence

The following query identifies Microsoft Background Intelligent Transfer Service utility bitsadmin.exe scheduling a BITS job to persist on an endpoint. The query identifies the parameters used to create, resume or add a file to a BITS job. Typically seen combined in a oneliner or ran in sequence. If identified, review the BITS job created and capture any files written to disk. It is possible for BITS to be used to upload files and this may require further network data analysis to identify. You can use bitsadmin /list /verbose to list out the jobs during investigation.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1197
• Last Updated: 2021-03-29

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=bitsadmin.exe Processes.process IN (*create*, *addfile*, *setnotifyflags*, *setnotifycmdline*, *setminretrydelay*, *setcustomheaders*, *resume* ) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `bits_job_persistence_filter`

Associated Analytic Story

• BITS Jobs

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1197	BITS Jobs	Defense Evasion, Persistence

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives will be present. Typically, applications will use BitsAdmin.exe. Any filtering should be done based on command-line arguments (legitimate applications) or parent process.

Reference

• https://attack.mitre.org/techniques/T1197/

• https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/bitsadmin

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md#atomic-test-3---persist-download--execute

• https://lolbas-project.github.io/lolbas/Binaries/Bitsadmin/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log

version: 1

---

BITSAdmin Download File

The following query identifies Microsoft Background Intelligent Transfer Service utility bitsadmin.exe using the transfer parameter to download a remote object. In addition, look for download or upload on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from bitsadmin.exe, but the artifacts will appear in a parallel process of svchost.exe with a command-line similar to svchost.exe -k netsvcs -s BITS. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use bitsadmin /list /verbose to list out the jobs during investigation.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1197, T1105
• Last Updated: 2021-03-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=bitsadmin.exe Processes.process=*transfer* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `bitsadmin_download_file_filter`

Associated Analytic Story

• Ingress Tool Transfer

• BITS Jobs

• DarkSide Ransomware

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1197	BITS Jobs	Defense Evasion, Persistence
T1105	Ingress Tool Transfer	Command and Control

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives, however it may be required to filter based on parent process name or network connection.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/8eb52117b748d378325f7719554a896e37bccec7/atomics/T1105/T1105.md#atomic-test-9---windows---bitsadmin-bits-download

• https://github.com/redcanaryco/atomic-red-team/blob/bc705cb7aaa5f26f2d96585fac8e4c7052df0ff9/atomics/T1197/T1197.md

• https://docs.microsoft.com/en-us/windows/win32/bits/bitsadmin-tool

• https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log

version: 1

---

Batch File Write to System32

The search looks for a batch file (.bat) written to the Windows system directory tree.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1204.002
• Last Updated: 2018-12-14

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)`
| rex field=file_name "(?<file_extension>\.[^\.]+)$" 
| search file_path=*system32* AND file_extension=.bat 
| `batch_file_write_to_system32_filter`

Associated Analytic Story

• SamSam Ransomware

How To Implement

You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

• Filesystem.dest

• Filesystem.file_name

• Filesystem.user

• Filesystem.file_path

ATT&CK

ID	Technique	Tactic
T1204.002	Malicious File	Execution

Kill Chain Phase

• Delivery

Known False Positives

It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log

version: 1

---

CMD Echo Pipe - Escalation

This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via jump (Cobalt Strike PTH) or getsystem, using named-pipe impersonation. A suspicious event will look like cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.003, T1543.003
• Last Updated: 2021-05-20

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=cmd.exe OR Processes.process=*%comspec%*) (Processes.process=*echo* AND Processes.process=*pipe*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `cmd_echo_pipe___escalation_filter`

Associated Analytic Story

• Cobalt Strike

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1059.003	Windows Command Shell	Execution
T1543.003	Windows Service	Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

• Privilege Escalation

Known False Positives

Unknown. It is possible filtering may be required to ensure fidelity.

Reference

• https://redcanary.com/threat-detection-report/threats/cobalt-strike/

• https://github.com/rapid7/meterpreter/blob/master/source/extensions/priv/server/elevate/namedpipe.c

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log

version: 1

---

CMLUA Or CMSTPLUA UAC Bypass

This analytic detects a potential process using COM Object like CMLUA or CMSTPLUA to bypass UAC. This technique has been used by ransomware adversaries to gain administrative privileges to its running process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.003
• Last Updated: 2021-05-13

details

Search

`sysmon` EventCode=7  ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll", "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*", "*\\program files*")) 
| stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name Computer EventCode Signed ProcessId 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `cmlua_or_cmstplua_uac_bypass_filter`

Associated Analytic Story

• DarkSide Ransomware

• Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Image

• ImageLoaded

• process_name

• Computer

• EventCode

• Signed

• ProcessId

ATT&CK

ID	Technique	Tactic
T1218.003	CMSTP	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Legitimate windows application that are not on the list loading this dll. Filter as needed.

Reference

• https://attack.mitre.org/techniques/T1218/003/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/darkside_cmstp_com/windows-sysmon.log

version: 1

---

CertUtil Download With URLCache and Split Arguments

Certutil.exe may download a file from a remote destination using -urlcache. This behavior does require a URL to be passed on the command-line. In addition, -f (force) and -split (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for certutil.exe to contact public IP space. However, it is uncommon for certutil.exe to write files to world writeable paths.\ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1105
• Last Updated: 2021-03-23

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process=*urlcache* Processes.process=*split* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `certutil_download_with_urlcache_and_split_arguments_filter`

Associated Analytic Story

• Ingress Tool Transfer

• DarkSide Ransomware

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1105	Ingress Tool Transfer	Command and Control

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.

Reference

• https://attack.mitre.org/techniques/T1105/

• https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats

• https://www.fireeye.com/blog/threat-research/2019/10/certutil-qualms-they-came-to-drop-fombs.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log

version: 1

---

CertUtil Download With VerifyCtl and Split Arguments

Certutil.exe may download a file from a remote destination using -VerifyCtl. This behavior does require a URL to be passed on the command-line. In addition, -f (force) and -split (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for certutil.exe to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using -VerifyCtl, the file will either be written to the current working directory or %APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1105
• Last Updated: 2021-03-23

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process=*verifyctl* Processes.process=*split* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `certutil_download_with_verifyctl_and_split_arguments_filter`

Associated Analytic Story

• Ingress Tool Transfer

• DarkSide Ransomware

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1105	Ingress Tool Transfer	Command and Control

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.

Reference

• https://attack.mitre.org/techniques/T1105/

• https://www.hexacorn.com/blog/2020/08/23/certutil-one-more-gui-lolbin/

• https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc732443(v=ws.11)#-verifyctl

• https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log

version: 1

---

CertUtil With Decode Argument

CertUtil.exe may be used to encode and decode a file, including PE and script code. Encoding will convert a file to base64 with -----BEGIN CERTIFICATE----- and -----END CERTIFICATE----- tags. Malicious usage will include decoding a encoded file that was downloaded. Once decoded, it will be loaded by a parallel process. Note that there are two additional command switches that may be used - encodehex and decodehex. Similarly, the file will be encoded in HEX and later decoded for further execution. During triage, identify the source of the file being decoded. Review its contents or execution behavior for further analysis.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1140
• Last Updated: 2021-03-23

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process=*decode* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `certutil_with_decode_argument_filter`

Associated Analytic Story

• Deobfuscate-Decode Files or Information

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1140	Deobfuscate/Decode Files or Information	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Typically seen used to encode files, but it is possible to see legitimate use of decode. Filter based on parent-child relationship, file paths, endpoint or user.

Reference

• https://attack.mitre.org/techniques/T1140/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1140/T1140.md

• https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/certutil

• https://www.bleepingcomputer.com/news/security/certutilexe-could-allow-attackers-to-download-malware-while-bypassing-av/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/windows-sysmon.log

version: 1

---

Certutil exe certificate extraction

This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK:
• Last Updated: 2021-01-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `certutil_exe_certificate_extraction_filter`

Associated Analytic Story

• Windows Persistence Techniques

• Cloud Federated Credential Abuse

How To Implement

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.parent_process

• Processes.user

Kill Chain Phase

• Installation

Known False Positives

Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log

version: 1

---

Child Processes of Spoolsv exe

This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1068
• Last Updated: 2020-03-16

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `child_processes_of_spoolsv_exe_filter` 

Associated Analytic Story

• Windows Privilege Escalation

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the children_of_spoolsv_filter macro to filter out legitimate child processes spawned by spoolsv.exe.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.process_name

• Processes.dest

• Processes.parent_process

• Processes.user

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search.

Reference

Test Dataset

version: 3

---

Clients Connecting to Multiple DNS Servers

This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1048.003
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src 
| `drop_dm_object_name("Network_Resolution")` 
|where dest_count > 5 
| `clients_connecting_to_multiple_dns_servers_filter` 

Associated Analytic Story

• DNS Hijacking

• Command and Control

• Suspicious DNS Traffic

• Host Redirection

How To Implement

This search requires that DNS data is being ingested and populating the Network_Resolution data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.
This search produces fields (dest_count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n1. Label: Distinct DNS Connections, Field: dest_count
Detailed documentation on how to create a new field within Incident Review may be found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

Required field

• _time

• DNS.dest

• DNS.message_type

• DNS.src

ATT&CK

ID	Technique	Tactic
T1048.003	Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol	Exfiltration

Kill Chain Phase

• Command and Control

Known False Positives

It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate.

Reference

Test Dataset

version: 3

---

Clop Common Exec Parameter

The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1204
• Last Updated: 2021-03-17

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name != "*temp.dat*" Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.parent_process_name Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `clop_common_exec_parameter_filter`

Associated Analytic Story

• Clop Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• Processes.process

• Processes.parent_process_name

• _time

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1204	User Execution	Execution

Kill Chain Phase

• Obfuscation

Known False Positives

Operators can execute third party tools using these parameters.

Reference

• https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

• https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log

version: 1

---

Clop Ransomware Known Service Name

This detection is to identify the common service name created by the CLOP ransomware as part of its persistence and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1543
• Last Updated: 2021-03-17

details

Search

`wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `clop_ransomware_known_service_name_filter`

Associated Analytic Story

• Clop Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.

Required field

• EventCode

• cmdline

• _time

• parent_process_name

• process_name

• OriginalFileName

• process_path

ATT&CK

ID	Technique	Tactic
T1543	Create or Modify System Process	Persistence, Privilege Escalation

Kill Chain Phase

• Privilege Escalation

Known False Positives

unknown

Reference

• https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

• https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log

version: 1

---

Cloud API Calls From Previously Unseen User Roles

This search looks for new commands from each user role.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078
• Last Updated: 2020-09-04

details

Search

| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenUserApiCall=min(firstTimeSeen) 
| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") 
| table firstTime, user, object, command 
|`security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `cloud_api_calls_from_previously_unseen_user_roles_filter`

Associated Analytic Story

• Suspicious Cloud User Activities

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud API Calls Per User Role - Initial to build the initial table of user roles, commands, and times. You must also enable the second baseline search Previously Seen Cloud API Calls Per User Role - Update to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the cloud_api_calls_from_previously_unseen_user_roles_activity_window macro. You can also provide additional filtering for this search by customizing the cloud_api_calls_from_previously_unseen_user_roles_filter

Required field

• _time

• All_Changes.user

• All_Changes.user_type

• All_Changes.status

• All_Changes.command

• All_Changes.object

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Compute Instance Created By Previously Unseen User

This search looks for cloud compute instances created by users who have not created them before.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078.004
• Last Updated: 2020-08-21

details

Search

| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenUser=min(firstTimeSeen) 
| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") 
| table firstTime, user, dest, count vendor_region 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_by_previously_unseen_user_filter`

Associated Analytic Story

• Cloud Cryptomining

How To Implement

You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users.

Required field

• _time

• All_Changes.object

• All_Changes.action

• All_Changes.user

• All_Changes.vendor_region

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Compute Instance Created In Previously Unused Region

This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1535
• Last Updated: 2020-09-02

details

Search

| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenRegion=min(firstTimeSeen) 
| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") 
| table firstTime, user, dest, count , vendor_region 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_in_previously_unused_region_filter`

Associated Analytic Story

• Cloud Cryptomining

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Regions - Initial to build the initial table of images observed and times. You must also enable the second baseline search Previously Seen Cloud Regions - Update to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the cloud_compute_instance_created_in_previously_unused_region_filter macro.

Required field

• _time

• All_Changes.object_id

• All_Changes.action

• All_Changes.vendor_region

• All_Changes.user

ATT&CK

ID	Technique	Tactic
T1535	Unused/Unsupported Cloud Regions	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Compute Instance Created With Previously Unseen Image

This search looks for cloud compute instances being created with previously unseen image IDs.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK:
• Last Updated: 2018-10-12

details

Search

| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| `drop_dm_object_name("Instance_Changes")` 
| where image_id != "unknown" 
| lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenImage=min(firstTimeSeen) 
| where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") 
| table firstTime, user, image_id, count, dest 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_with_previously_unseen_image_filter`

Associated Analytic Story

• Cloud Cryptomining

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Compute Images - Initial to build the initial table of images observed and times. You must also enable the second baseline search Previously Seen Cloud Compute Images - Update to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the cloud_compute_instance_created_with_previously_unseen_image_filter macro.

Required field

• _time

• All_Changes.object_id

• All_Changes.action

• All_Changes.Instance_Changes.image_id

• All_Changes.user

Kill Chain Phase

Known False Positives

After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Compute Instance Created With Previously Unseen Instance Type

Find EC2 instances being created with previously unseen instance types.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK:
• Last Updated: 2020-09-12

details

Search

| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| `drop_dm_object_name("Instance_Changes")` 
| where instance_type != "unknown" 
| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenInstanceType=min(firstTimeSeen) 
| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") 
| table firstTime, user, dest, count, instance_type 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`

Associated Analytic Story

• Cloud Cryptomining

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Compute Instance Types - Initial to build the initial table of instance types observed and times. You must also enable the second baseline search Previously Seen Cloud Compute Instance Types - Update to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the cloud_compute_instance_created_with_previously_unseen_instance_type_filter macro.

Required field

• _time

• All_Changes.object_id

• All_Changes.action

• All_Changes.Instance_Changes.instance_type

• All_Changes.user

Kill Chain Phase

Known False Positives

It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Instance Modified By Previously Unseen User

This search looks for cloud instances being modified by users who have not previously modified them.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078.004
• Last Updated: 2020-07-29

details

Search

| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenUser=min(firstTimeSeen) 
| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") 
| table firstTime user command object_id count 
| `security_content_ctime(firstTime)` 
| `cloud_instance_modified_by_previously_unseen_user_filter`

Associated Analytic Story

• Suspicious Cloud Instance Activities

How To Implement

This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work.

Required field

• _time

• All_Changes.object_id

• All_Changes.command

• All_Changes.action

• All_Changes.change_type

• All_Changes.status

• All_Changes.user

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Network Access Control List Deleted

Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-09-08

details

Search

`cloudtrail` eventName=DeleteNetworkAcl
|rename userIdentity.arn as arn  
| stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `cloud_network_access_control_list_deleted_filter`

Associated Analytic Story

• Cloud Network ACL Activity

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the cloud_network_access_control_list_deleted_filter macro.

Required field

• _time

• eventName

• userIdentity.arn

• errorMessage

• errorCode

• userAgent

• src

• userName

• arn

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's possible that a user has legitimately deleted a network ACL.

Reference

Test Dataset

version: 1

---

Cloud Provisioning Activity From Previously Unseen City

This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078
• Last Updated: 2020-10-09

details

Search

| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(City) 
| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenCity=min(firstTimeSeen) 
| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) 
| table firstTime, src, City, user, object, command 
| `cloud_provisioning_activity_from_previously_unseen_city_filter` 
| `security_content_ctime(firstTime)`

Associated Analytic Story

• Suspicious Cloud Provisioning Activities

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Provisioning Activity Sources - Initial to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search Previously Seen Cloud Provisioning Activity Sources - Update to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the previously_unseen_cloud_provisioning_activity_window macro. You can also provide additional filtering for this search by customizing the cloud_provisioning_activity_from_previously_unseen_city_filter macro.

Required field

• _time

• All_Changes.action

• All_Changes.status

• All_Changes.src

• All_Changes.user

• All_Changes.object

• All_Changes.command

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
This search will fire any time a new IP address is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Provisioning Activity From Previously Unseen Country

This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078
• Last Updated: 2020-10-09

details

Search

| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(Country) 
| lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenCountry=min(firstTimeSeen) 
| where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") 
| table firstTime, src, Country, user, object, command 
| `cloud_provisioning_activity_from_previously_unseen_country_filter` 
| `security_content_ctime(firstTime)`

Associated Analytic Story

• Suspicious Cloud Provisioning Activities

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Provisioning Activity Sources - Initial to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search Previously Seen Cloud Provisioning Activity Sources - Update to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the previously_unseen_cloud_provisioning_activity_window macro. You can also provide additional filtering for this search by customizing the cloud_provisioning_activity_from_previously_unseen_country_filter macro.

Required field

• _time

• All_Changes.action

• All_Changes.status

• All_Changes.src

• All_Changes.user

• All_Changes.object

• All_Changes.command

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
This search will fire any time a new IP address is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Provisioning Activity From Previously Unseen IP Address

This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078
• Last Updated: 2020-08-16

details

Search

| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenSrc=min(firstTimeSeen) 
| where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) 
| table firstTime, src, user, object_id, command 
| `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` 
| `security_content_ctime(firstTime)`

Associated Analytic Story

• Suspicious Cloud Provisioning Activities

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Provisioning Activity Sources - Initial to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search Previously Seen Cloud Provisioning Activity Sources - Update to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the previously_unseen_cloud_provisioning_activity_window macro. You can also provide additional filtering for this search by customizing the cloud_provisioning_activity_from_previously_unseen_ip_address_filter macro.

Required field

• _time

• All_Changes.object_id

• All_Changes.action

• All_Changes.status

• All_Changes.src

• All_Changes.user

• All_Changes.command

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
This search will fire any time a new IP address is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cloud Provisioning Activity From Previously Unseen Region

This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078
• Last Updated: 2020-08-16

details

Search

| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(Region) 
| lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenRegion=min(firstTimeSeen) 
| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) 
| table firstTime, src, Region, user, object, command 
| `cloud_provisioning_activity_from_previously_unseen_region_filter` 
| `security_content_ctime(firstTime)`

Associated Analytic Story

• Suspicious Cloud Provisioning Activities

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Provisioning Activity Sources - Initial to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search Previously Seen Cloud Provisioning Activity Sources - Update to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the previously_unseen_cloud_provisioning_activity_window macro. You can also provide additional filtering for this search by customizing the cloud_provisioning_activity_from_previously_unseen_region_filter macro.

Required field

• _time

• All_Changes.action

• All_Changes.status

• All_Changes.src

• All_Changes.user

• All_Changes.object

• All_Changes.command

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
This search will fire any time a new IP address is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Cobalt Strike Named Pipes

The following analytic identifies the use of default or publicly known named pipes used with Cobalt Strike. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. Cobalt Strike uses named pipes in many ways and has default values used with the Artifact Kit and Malleable C2 Profiles. The following query assists with identifying these default named pipes. Each EDR product presents named pipes a little different. Consider taking the values and generating a query based on the product of choice.
Upon triage, review the process performing the named pipe. If it is explorer.exe, It is possible it was injected into by another process. Review recent parallel processes to identify suspicious patterns or behaviors. A parallel process may have a network connection, review and follow the connection back to identify any file modifications.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1055
• Last Updated: 2021-02-22

details

Search

`sysmon` EventID=17 OR EventID=18 PipeName IN (\\msagent_*, \\wkssvc*, \\DserNamePipe*, \\srvsvc_*, \\mojo.*, \\postex_*, \\status_*, \\MSSE-*, \\spoolss_*, \\win_svc*, \\ntsvcs*, \\winsock*, \\UIA_PIPE*) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, process_name, process_id process_path, PipeName 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `cobalt_strike_named_pipes_filter`

Associated Analytic Story

• Cobalt Strike

• Trickbot

• DarkSide Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• PipeName

• Computer

• process_name

• process_path

• process_id

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

The idea of using named pipes with Cobalt Strike is to blend in. Therefore, some of the named pipes identified and added may cause false positives. Filter by process name or pipe name to reduce false positives.

Reference

• https://attack.mitre.org/techniques/T1218/009/

• https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes

• https://www.cobaltstrike.com/help-smb-beacon

• https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/

• https://gist.github.com/MHaggis/6c600e524045a6d49c35291a21e10752

• https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log

version: 1

---

Common Ransomware Extensions

The search looks for file modifications with extensions commonly used by Ransomware

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1485
• Last Updated: 2020-11-09

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)`
| rex field=file_name "(?<file_extension>\.[^\.]+)$" 
| `ransomware_extensions` 
| `common_ransomware_extensions_filter`

Associated Analytic Story

• SamSam Ransomware

• Ryuk Ransomware

• Ransomware

• Clop Ransomware

How To Implement

You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
This search produces fields (query,query_length,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n1. Label: Name, Field: Name\

1. \
2. Label: File Extension, Field: file_extension
   Detailed documentation on how to create a new field within Incident Review may be found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

Required field

• _time

• Filesystem.user

• Filesystem.dest

• Filesystem.file_path

• Filesystem.file_name

ATT&CK

ID	Technique	Tactic
T1485	Data Destruction	Impact

Kill Chain Phase

• Actions on Objectives

Known False Positives

It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_extensions/windows-sysmon.log

version: 4

---

Common Ransomware Notes

The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1485
• Last Updated: 2020-11-09

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `ransomware_notes` 
| `common_ransomware_notes_filter`

Associated Analytic Story

• SamSam Ransomware

• Ransomware

• Ryuk Ransomware

• Clop Ransomware

How To Implement

You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.

Required field

• _time

• Filesystem.user

• Filesystem.dest

• Filesystem.file_path

• Filesystem.file_name

ATT&CK

ID	Technique	Tactic
T1485	Data Destruction	Impact

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's possible that a legitimate file could be created with the same name used by ransomware note files.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log

version: 4

---

Conti Common Exec parameter

This search detects the suspicious commandline argument of revil ransomware to encrypt specific or all local drive and network shares of the compromised machine or host.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1204
• Last Updated: 2021-06-02

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-m local*" OR Processes.process = "*-m net*" OR Processes.process = "*-m all*" OR Processes.process = "*-nomutex*" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `conti_common_exec_parameter_filter`

Associated Analytic Story

• Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.parent_process

• Processes.dest Processes.user

• Processes.process_id

• Processes.process_guid

ATT&CK

ID	Technique	Tactic
T1204	User Execution	Execution

Kill Chain Phase

• Exploitation

Known False Positives

3rd party tool may have commandline parameter that can trigger this detection.

Reference

• https://malpedia.caad.fkie.fraunhofer.de/details/win.conti

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/inf1/windows-sysmon.log

version: 1

---

Create Remote Thread into LSASS

Detect remote thread creation into LSASS consistent with credential dumping.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1003.001
• Last Updated: 2019-12-06

details

Search

`sysmon` EventID=8 TargetImage=*lsass.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `create_remote_thread_into_lsass_filter`

Associated Analytic Story

• Credential Dumping

How To Implement

This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named sysmon. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

Required field

• _time

• EventID

• TargetImage

• Computer

• EventCode

• TargetImage

• TargetProcessId

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise.

Reference

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log

version: 1

---

Create Service In Suspicious File Path

This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1569.001, T1569.002
• Last Updated: 2021-03-12

details

Search

 `wineventlog_system` EventCode=7045  Service_File_Name = "*\.exe" NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) Service_Type = "user mode service" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `create_service_in_suspicious_file_path_filter`

Associated Analytic Story

• Clop Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.

Required field

• EventCode

• Service_File_Name

• Service_Type

• _time

• Service_Name

• Service_Start_Type

ATT&CK

ID	Technique	Tactic
T1569.001	Launchctl	Execution
T1569.002	Service Execution	Execution

Kill Chain Phase

• Privilege Escalation

Known False Positives

unknown

Reference

• https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

• https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log

version: 1

---

Create local admin accounts using net exe

This search looks for the creation of local administrator accounts using net.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1136.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`create_local_admin_accounts_using_net_exe_filter` 

Associated Analytic Story

• DHS Report TA18-074A

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1136.001	Local Account	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators often leverage net.exe to create admin accounts.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log

version: 4

---

Create or delete windows shares using net exe

This search looks for the creation or deletion of hidden shares using net.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1070.005
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| search process=*share* 
| `create_or_delete_windows_shares_using_net_exe_filter` 

Associated Analytic Story

• Hidden Cobra Malware

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.user

• Processes.parent_process

• Processs.process_name

• Processes.process

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1070.005	Network Share Connection Removal	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate.

Reference

• https://attack.mitre.org/techniques/T1070/005

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log

version: 5

---

Creation of Shadow Copy

Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.003
• Last Updated: 2019-12-10

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `creation_of_shadow_copy_filter`

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1003.003	NTDS	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Legitimate administrator usage of Vssadmin or Wmic will create false positives.

Reference

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log

version: 1

---

Creation of Shadow Copy with wmic and powershell

This search detects the use of wmic and Powershell to create a shadow copy.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.003
• Last Updated: 2019-12-10

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `creation_of_shadow_copy_with_wmic_and_powershell_filter`

Associated Analytic Story

• Credential Dumping

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1003.003	NTDS	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Legtimate administrator usage of wmic to create a shadow copy.

Reference

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log

version: 1

---

Creation of lsass Dump with Taskmgr

Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1003.001
• Last Updated: 2020-02-03

details

Search

`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `creation_of_lsass_dump_with_taskmgr_filter`

Associated Analytic Story

• Credential Dumping

How To Implement

This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named sysmon. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

Required field

• _time

• EventID

• process_name

• TargetFilename

• Computer

• object_category

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager

• https://attack.mitre.org/techniques/T1003/001/

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log

version: 1

---

Credential Dumping via Copy Command from Shadow Copy

This search detects credential dumping using copy command from a shadow copy.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.003
• Last Updated: 2019-12-10

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `credential_dumping_via_copy_command_from_shadow_copy_filter` 

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1003.003	NTDS	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

unknown

Reference

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log

version: 1

---

Credential Dumping via Symlink to Shadow Copy

This search detects the creation of a symlink to a shadow copy.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.003
• Last Updated: 2019-12-10

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `credential_dumping_via_symlink_to_shadow_copy_filter` 

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1003.003	NTDS	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

unknown

Reference

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log

version: 1

---

Credential Extraction indicative of FGDump and CacheDump with s option

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-18

details

Search

 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) 
| where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Unusual Processes

• Credential Dumping

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• process_name

• parent_process_name

• _time

• process_path

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logFgdump.log

version: 1

---

Credential Extraction indicative of FGDump and CacheDump with v option

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-18

details

Search

 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null) 
| where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Unusual Processes

• Credential Dumping

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• process_name

• _time

• process_path

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logFgdump.log

version: 1

---

Credential Extraction indicative of Lazagne command line options

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003, T1555
• Last Updated: 2020-10-18

details

Search

 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access
T1555	Credentials from Password Stores	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logLazagneCredDump.log

version: 1

---

Credential Extraction indicative of use of DSInternals credential conversion modules

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-21

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

• Malicious PowerShell

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• process_name

• parent_process_name

• _time

• process_path

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/MichaelGrafnetter/DSInternals

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllDSInternalsModules.log

version: 1

---

Credential Extraction indicative of use of DSInternals modules

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-21

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

• Malicious PowerShell

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• process_name

• parent_process_name

• _time

• process_path

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/MichaelGrafnetter/DSInternals

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllDSInternalsModules.log

version: 1

---

Credential Extraction indicative of use of Mimikatz modules

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-21

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

• Unusual Processes

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllMimikatzModules.log

version: 1

---

Credential Extraction indicative of use of PowerSploit modules

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-21

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

• Malicious PowerShell

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllPowerSploitModulesWithOldNames.log

version: 1

---

Credential Extraction native Microsoft debuggers peek into the kernel

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-18

details

Search

 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) 
| where cmd_line != null AND parent_process_name != null AND process_name != null  AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

• Unusual Processes

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• process_name

• parent_process_name

• _time

• dest_device_id

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps.

Reference

• https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logLiveKDFullKernelDump.log

version: 1

---

Credential Extraction native Microsoft debuggers via z command line option

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-18

details

Search

 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) 
| where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

• Unusual Processes

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• process_name

• _time

• dest_device_id

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logLiveKDFullKernelDump.log

version: 1

---

Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals

Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003
• Last Updated: 2020-10-18

details

Search

 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\
|]+/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

• Malicious PowerShell

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1003	OS Credential Dumping	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logPowerShellModule.log

version: 1

---

DLLHost with no Command Line Arguments with Network

The following analytic identifies DLLHost.exe with no command line arguments with a network connection. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1055
• Last Updated: 2021-04-19

details

Search

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| regex process="(dllhost\.exe.{0,4}$)" 
| join  process_id [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port 
| `drop_dm_object_name(Ports)` 
| rename  dest as connection_to_CNC] 
| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port 
| `dllhost_with_no_command_line_arguments_with_network_filter`

Associated Analytic Story

• Cobalt Strike

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes and port node.

Required field

• _time

• EventID

• process_name

• process_id

• parent_process_name

• dest_port

• process_path

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate third party applications may use a moved copy of dllhost, triggering a false positive.

Reference

• https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile

• https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_dllhost.log

version: 1

---

DNS Exfiltration Using Nslookup App

this search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type, TXT, A, AAAA, that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1048
• Last Updated: 2021-04-15

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*" OR Processes.process="*-qt=*" OR Processes.process="*-q=*" OR Processes.process="-type=*" OR Processes.process="*-retry=*" by Processes.dest Processes.user Processes.process_name  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dns_exfiltration_using_nslookup_app_filter`

Associated Analytic Story

• Suspicious DNS Traffic

• Dynamic DNS

• Command and Control

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of nslookup.exe may be used.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1048	Exfiltration Over Alternative Protocol	Exfiltration

Kill Chain Phase

• Exploitation

Known False Positives

admin nslookup usage

Reference

• https://www.fireeye.com/blog/threat-research/2017/03/fin7_spear_phishing.html

• https://www.varonis.com/blog/dns-tunneling/

• https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/windows-sysmon.log

version: 1

---

DNS Query Length Outliers - MLTK

This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1071.004
• Last Updated: 2020-01-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type 
| search DNS.record_type=* 
|  `drop_dm_object_name(DNS)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| eval query_length = len(query) 
| apply dns_query_pdfmodel threshold=0.01 
| rename "IsOutlier(query_length)" as isOutlier 
| search isOutlier > 0 
| sort -query_length 
| table start_time end_time query record_type count src dest query_length 
| `dns_query_length_outliers___mltk_filter` 

Associated Analytic Story

• Hidden Cobra Malware

• Suspicious DNS Traffic

• Command and Control

How To Implement

To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.
This search produces fields (query,query_length,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n1. Label: DNS Query, Field: query\

1. \
2. Label: DNS Query Length, Field: query_length\
3. \
4. Label: Number of events, Field: count
   Detailed documentation on how to create a new field within Incident Review may be found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

Required field

• _time

• DNS.src

• DNS.dest

• DNS.query

• DNS.record_type

ATT&CK

ID	Technique	Tactic
T1071.004	DNS	Command and Control

Kill Chain Phase

• Command and Control

Known False Positives

If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.

Reference

Test Dataset

version: 2

---

DNS Query Length With High Standard Deviation

This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1048.003
• Last Updated: 2021-01-18

details

Search

| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query 
|  `drop_dm_object_name("DNS")` 
| eval query_length = len(query) 
| table query query_length record_type count 
| eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50
| where query_length>(avg+stdev*2) 
| eval z_score=(query_length-avg)/stdev 
| `dns_query_length_with_high_standard_deviation_filter` 

Associated Analytic Story

• Hidden Cobra Malware

• Suspicious DNS Traffic

• Command and Control

How To Implement

To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model.

Required field

• _time

• DNS.query

ATT&CK

ID	Technique	Tactic
T1048.003	Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol	Exfiltration

Kill Chain Phase

• Command and Control

Known False Positives

It's possible there can be long domain names that are legitimate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log

version: 3

---

DNS Query Requests Resolved by Unauthorized DNS Servers

This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1071.004
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest 
| `drop_dm_object_name("DNS")` 
| `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` 

Associated Analytic Story

• DNS Hijacking

• Command and Control

• Suspicious DNS Traffic

• Host Redirection

How To Implement

To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security.

Required field

• _time

• DNS.dest_category

• DNS.src_category

• DNS.src

• DNS.dest

ATT&CK

ID	Technique	Tactic
T1071.004	DNS	Command and Control

Kill Chain Phase

• Command and Control

Known False Positives

Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate.

Reference

Test Dataset

version: 3

---

DNS record changed

The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1071.004
• Last Updated: 2020-07-21

details

Search

| inputlookup discovered_dns_records 
| rename answer as discovered_answer 
| join domain[
|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query 
| rename DNS.query as query 
| where query!="unknown" 
| rex field=query "(?<domain>\w+\.\w+?)(?:$
|/)"] 
| makemv delim=" " answer 
|  makemv delim=" " type 
| sort -count 
| table count,src,domain,type,query,current_answer,discovered_answer 
| makemv current_answer  
| mvexpand current_answer 
| makemv discovered_answer 
| eval n=mvfind(discovered_answer, current_answer) 
| where isnull(n) 
| `dns_record_changed_filter`

Associated Analytic Story

• DNS Hijacking

How To Implement

To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that the discover_dns_record lookup table be populated by the included support search "Discover DNS record".
Splunk>Phantom Playbook Integration
If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk https://splunkbase.splunk.com/app/3411/, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active.
(Playbook Link:https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/).\

Required field

• _time

• DNS.record_type

• DNS.answer

• DNS.src

• DNS.message_type

• DNS.query

ATT&CK

ID	Technique	Tactic
T1071.004	DNS	Command and Control

Kill Chain Phase

• Command and Control

Known False Positives

Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate.

Reference

Test Dataset

version: 3

---

DSQuery Domain Discovery

The following analytic identifies "dsquery.exe" execution with arguments looking for TrustedDomain query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.
Within this detection, it is assumed dsquery.exe is not moved or renamed.
The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.
DSQuery.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64 and only on Server operating system.
The following DLL(s) are loaded when DSQuery.exe is launched dsquery.dll. If found loaded by another process, it is possible dsquery is running within that process context in memory.
In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1482
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dsquery.exe Processes.process=*trustedDomain* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dsquery_domain_discovery_filter`

Associated Analytic Story

• Domain Trust Discovery

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1482	Domain Trust Discovery	Discovery

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives. If there is a true false positive, filter based on command-line or parent process.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md

• http://www.harmj0y.net/blog/redteaming/a-guide-to-attacking-domain-trusts/

• https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc732952(v=ws.11)

• https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc754232(v=ws.11)

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log

version: 1

---

Delete ShadowCopy With PowerShell

This following analytic detects PowerShell command to delete shadow copy using the WMIC PowerShell module. This technique was seen used by a recent adversary to deploy DarkSide Ransomware where it executed a child process of PowerShell to execute a hex encoded command to delete shadow copy. This hex encoded command was able to be decrypted by PowerShell log.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1490
• Last Updated: 2021-05-12

details

Search

`powershell` EventCode=4104 Message= "*ShadowCopy*" Message = "*Delete*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `delete_shadowcopy_with_powershell_filter`

Associated Analytic Story

• DarkSide Ransomware

• Ransomware

• Revil Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event.

Required field

• _time

• EventCode

• Message

• ComputerName

• User

ATT&CK

ID	Technique	Tactic
T1490	Inhibit System Recovery	Impact

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

• https://searchwindowsserver.techtarget.com/tutorial/Set-up-PowerShell-script-block-logging-for-added-security

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-powershell.log

version: 1

---

Deleting Of Net Users

This analytic will detect a suspicious net.exe/net1.exe command-line to delete a user on a system. This technique may be use by an administrator for legitimate purposes, however this behavior has been used in the wild to impair some user or deleting adversaries tracks created during its lateral movement additional systems. During triage, review parallel processes for additional behavior. Identify any other user accounts created before or after.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1531
• Last Updated: 2021-05-04

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND Processes.process="*user*" AND  Processes.process="*/delete*" by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `deleting_of_net_users_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed net.exe may be used.

Required field

• _time

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.parent_process_name

• Processes.process_id

• Processes.parent_process

ATT&CK

ID	Technique	Tactic
T1531	Account Access Removal	Impact

Kill Chain Phase

• Exploitation

Known False Positives

System administrators or scripts may delete user accounts via this technique. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Deleting Shadow Copies

The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1490
• Last Updated: 2020-11-09

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `deleting_shadow_copies_filter`

Associated Analytic Story

• Windows Log Manipulation

• SamSam Ransomware

• Ransomware

• Clop Ransomware

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.parent_process_name

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1490	Inhibit System Recovery	Impact

Kill Chain Phase

• Actions on Objectives

Known False Positives

vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log

version: 4

---

Detect API activity from users without MFA

This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2018-05-17

details

Search

`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false 
| search NOT [
| inputlookup aws_service_accounts 
| fields identity 
| rename identity as user]
| stats  count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user 
| `security_content_ctime(firstTime)`  
| `security_content_ctime(lastTime)` 
| `detect_api_activity_from_users_without_mfa_filter`

Associated Analytic Story

• AWS User Monitoring

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search Create a list of approved AWS service accounts: run it once every 30 days to create a list of service accounts and validate them.
This search produces fields (eventName,userIdentity.type,userIdentity.arn) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n1. Label: AWS Event Name, Field: eventName\

1. \
2. Label: AWS User ARN, Field: userIdentity.arn\
3. \
4. Label: AWS User Type, Field: userIdentity.type
   Detailed documentation on how to create a new field within Incident Review may be found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

Required field

• _time

• userIdentity.sessionContext.attributes.mfaAuthenticated

• eventName

• userIdentity.arn

• userIdentity.type

• user

Kill Chain Phase

Known False Positives

Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS.

Reference

Test Dataset

version: 1

---

Detect ARP Poisoning

By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1200, T1498, T1557.002
• Last Updated: 2020-08-11

details

Search

`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" 
| eval src_interface=src_int_prefix_long+src_int_suffix 
| stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)`
| `detect_arp_poisoning_filter`

Associated Analytic Story

• Router and Infrastructure Security

How To Implement

This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.

Required field

• _time

• facility

• mnemonic

• disable_cause

• src_int_prefix_long

• src_int_suffix

• host

• src_interface

ATT&CK

ID	Technique	Tactic
T1200	Hardware Additions	Initial Access
T1498	Network Denial of Service	Impact
T1557.002	ARP Cache Poisoning	Collection, Credential Access

Kill Chain Phase

• Reconnaissance

• Delivery

• Actions on Objectives

Known False Positives

This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely).

Reference

Test Dataset

version: 1

---

Detect AWS API Activities From Unapproved Accounts

This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or aws_service_accounts.csv. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` errorCode=success 
| rename userName as identity 
| search NOT [
| inputlookup identity_lookup_expanded 
| fields identity] 
| search NOT [
| inputlookup aws_service_accounts 
| fields identity] 
| rename identity as user 
| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_aws_api_activities_from_unapproved_accounts_filter`

Associated Analytic Story

• AWS User Monitoring

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the identity_lookup_expanded lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.
This search produces fields (eventName,firstTime,lastTime) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n1. Label: AWS Event Name, Field: eventName\

1. \
2. Label: First Time, Field: firstTime\
3. \
4. Label: Last Time, Field: lastTime
   Detailed documentation on how to create a new field within Incident Review may be found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

Required field

• _time

• errorCode

• userName

• eventName

• user

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's likely that you'll find activity detected by users/service accounts that are not listed in the identity_lookup_expanded or aws_service_accounts.csv file. If the user is a legitimate service account, update the aws_service_accounts.csv table with that entry.

Reference

Test Dataset

version: 2

---

Detect AWS Console Login by New User

This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Authentication
• ATT&CK:
• Last Updated: 2020-05-28

details

Search

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user 
| `drop_dm_object_name(Authentication)` 
| inputlookup append=t previously_seen_users_console_logins 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by user 
| eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") 
|where userStatus="First Time Logging into AWS Console" 
|  `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `detect_aws_console_login_by_new_user_filter`

Associated Analytic Story

• Suspicious Cloud Authentication Activities

How To Implement

You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the Previously Seen Users in CloudTrail - Initial support search only once to create a baseline of previously seen IAM users within the last 30 days. Run Previously Seen Users in CloudTrail - Update hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.

Required field

• _time

• Authentication.signature

• Authentication.user

Kill Chain Phase

• Actions on Objectives

Known False Positives

When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Detect AWS Console Login by User from New City

This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Authentication
• ATT&CK: T1535
• Last Updated: 2020-10-07

details

Search

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src 
| iplocation Authentication.src 
| `drop_dm_object_name(Authentication)` 
| table firstTime lastTime user City 
| join user  type=outer [
| inputlookup previously_seen_users_console_logins 
| stats earliest(firstTime) AS earliestseen by user City 
| fields earliestseen user City] 
| eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") 
| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") 
| where userCity = "New City" AND userStatus != "Old User" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table firstTime lastTime user City  userStatus userCity  
| `detect_aws_console_login_by_user_from_new_city_filter`

Associated Analytic Story

• Suspicious AWS Login Activities

• Suspicious Cloud Authentication Activities

How To Implement

You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the Previously Seen Users in CloudTrail - Initial support search only once to create a baseline of previously seen IAM users within the last 30 days. Run Previously Seen Users in CloudTrail - Update hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the detect_aws_console_login_by_user_from_new_city_filter macro.

Required field

• _time

• Authentication.signature

• Authentication.user

• Authentication.src

ATT&CK

ID	Technique	Tactic
T1535	Unused/Unsupported Cloud Regions	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Detect AWS Console Login by User from New Country

This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Authentication
• ATT&CK: T1535
• Last Updated: 2020-10-07

details

Search

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src 
| iplocation Authentication.src 
| `drop_dm_object_name(Authentication)` 
| table firstTime lastTime user Country 
| join user  type=outer [
| inputlookup previously_seen_users_console_logins 
| stats earliest(firstTime) AS earliestseen by user Country 
| fields earliestseen user Country] 
| eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") 
| eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") 
| where userCountry = "New Country" AND userStatus != "Old User" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table firstTime lastTime user Country  userStatus userCountry  
| `detect_aws_console_login_by_user_from_new_country_filter`

Associated Analytic Story

• Suspicious AWS Login Activities

• Suspicious Cloud Authentication Activities

How To Implement

You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the Previously Seen Users in CloudTrail - Initial support search only once to create a baseline of previously seen IAM users within the last 30 days. Run Previously Seen Users in CloudTrail - Update hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the detect_aws_console_login_by_user_from_new_country_filter macro.

Required field

• _time

• Authentication.signature

• Authentication.user

• Authentication.src

ATT&CK

ID	Technique	Tactic
T1535	Unused/Unsupported Cloud Regions	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Detect AWS Console Login by User from New Region

This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Authentication
• ATT&CK: T1535
• Last Updated: 2020-10-07

details

Search

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src 
| iplocation Authentication.src 
| `drop_dm_object_name(Authentication)` 
| table firstTime lastTime user Region 
| join user  type=outer [
| inputlookup previously_seen_users_console_logins 
| stats earliest(firstTime) AS earliestseen by user Region 
| fields earliestseen user Region] 
| eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") 
| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") 
| where userRegion = "New Region" AND userStatus != "Old User" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table firstTime lastTime user Region  userStatus userRegion  
| `detect_aws_console_login_by_user_from_new_region_filter`

Associated Analytic Story

• Suspicious AWS Login Activities

• Suspicious Cloud Authentication Activities

How To Implement

You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the Previously Seen Users in CloudTrail - Initial support search only once to create a baseline of previously seen IAM users within the last 30 days. Run Previously Seen Users in CloudTrail - Update hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the detect_aws_console_login_by_user_from_new_region_filter macro.

Required field

• _time

• Authentication.signature

• Authentication.user

• Authentication.src

ATT&CK

ID	Technique	Tactic
T1535	Unused/Unsupported Cloud Regions	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json

version: 1

---

Detect Activity Related to Pass the Hash Attacks

This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1550.002
• Last Updated: 2020-10-15

details

Search

`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) 
| fillnull 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_activity_related_to_pass_the_hash_attacks_filter` 

Associated Analytic Story

• Lateral Movement

How To Implement

To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows.

Required field

• _time

• EventCode

• Logon_Type

• Logon_Process

• WorkstationName

• user

• dest

ATT&CK

ID	Technique	Tactic
T1550.002	Pass the Hash	Defense Evasion, Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log

version: 5

---

Detect AzureHound Command-Line Arguments

The following analytic identifies the common command-line argument used by AzureHound Invoke-AzureHound. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1087.002, T1087.001, T1482, T1069.002, T1069.001
• Last Updated: 2021-06-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*invoke-azurehound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_azurehound_command_line_arguments_filter`

Associated Analytic Story

• Discovery Techniques

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1087.002	Domain Account	Discovery
T1087.001	Local Account	Discovery
T1482	Domain Trust Discovery	Discovery
T1069.002	Domain Groups	Discovery
T1069.001	Local Groups	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

Unknown.

Reference

• https://attack.mitre.org/software/S0521/

• https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

• https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350

• https://github.com/BloodHoundAD/BloodHound/blob/master/Collectors/AzureHound.ps1

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log

version: 1

---

Detect AzureHound File Modifications

The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop 20210601090751-azurecollection.zip. In addition to the zip, multiple .json files will be written to disk, which are in the zip.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1087.002, T1087.001, T1482, T1069.002, T1069.001
• Last Updated: 2021-06-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*-azurecollection.zip", "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", "*-azapplicationadmins.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_azurehound_file_modifications_filter`

Associated Analytic Story

• Discovery Techniques

How To Implement

To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the Endpoint datamodel in the Filesystem node.

Required field

• _time

• file_path

• dest

• file_name

• process_id

• file_create_time

ATT&CK

ID	Technique	Tactic
T1087.002	Domain Account	Discovery
T1087.001	Local Account	Discovery
T1482	Domain Trust Discovery	Discovery
T1069.002	Domain Groups	Discovery
T1069.001	Local Groups	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.

Reference

• https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350

• https://raw.githubusercontent.com/BloodHoundAD/BloodHound/master/Collectors/AzureHound.ps1

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log

version: 1

---

Detect Baron Samedit CVE-2021-3156

This search detects the heap-based buffer overflow of sudoedit

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1068
• Last Updated: 2021-01-27

details

Search

`linux_hosts` 
| search "sudoedit -s \\" 
| `detect_baron_samedit_cve_2021_3156_filter`

Associated Analytic Story

• Baron Samedit CVE-2021-3156

How To Implement

Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit

Test Dataset

version: 1

---

Detect Baron Samedit CVE-2021-3156 Segfault

This search detects the heap-based buffer overflow of sudoedit

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1068
• Last Updated: 2021-01-29

details

Search

`linux_hosts` 
| search sudoedit segfault 
| stats count min(_time) as firstTime max(_time) as lastTime by host 
| search count > 5 
| `detect_baron_samedit_cve_2021_3156_segfault_filter`

Associated Analytic Story

• Baron Samedit CVE-2021-3156

How To Implement

Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host

Required field

• _time

• host

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

If sudoedit is throwing segfaults for other reasons this will pick those up too.

Reference

• https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit

Test Dataset

version: 1

---

Detect Baron Samedit CVE-2021-3156 via OSQuery

This search detects the heap-based buffer overflow of sudoedit

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1068
• Last Updated: 2021-01-28

details

Search

`osquery_process` 
| search "columns.cmdline"="sudoedit -s \\*" 
| `detect_baron_samedit_cve_2021_3156_via_osquery_filter`

Associated Analytic Story

• Baron Samedit CVE-2021-3156

How To Implement

OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.

Required field

• _time

• columns.cmdline

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit

Test Dataset

version: 1

---

Detect Computer Changed with Anonymous Account

This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1210
• Last Updated: 2020-09-18

details

Search

`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 
| stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user 
| `detect_computer_changed_with_anonymous_account_filter`

Associated Analytic Story

• Detect Zerologon Attack

How To Implement

This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

Required field

• _time

• EventCode

• TargetUserName

• LogonType

• TargetDomainName

• user

ATT&CK

ID	Technique	Tactic
T1210	Exploitation of Remote Services	Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

None thus far found

Reference

• https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/

Test Dataset

version: 1

---

Detect Credential Dumping through LSASS access

This search looks for reading lsass memory consistent with credential dumping.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1003.001
• Last Updated: 2019-12-03

details

Search

`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_credential_dumping_through_lsass_access_filter` 

Associated Analytic Story

• Credential Dumping

• Detect Zerologon Attack

How To Implement

This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named sysmon. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

Required field

• _time

• EventCode

• TargetImage

• GrantedAccess

• Computer

• SourceImage

• SourceProcessId

• TargetImage

• TargetProcessId

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log

version: 3

---

Detect DNS requests to Phishing Sites leveraging EvilGinx2

This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1566.003
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host 
| `drop_dm_object_name(DNS)`
| rex field=query ".*?(?<domain>[^./:]+\.(\S{2,3}
|\S{2,3}.\S{2,3}))$" 
| stats count values(query) as query by domain dest src answer
| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` 
| search NOT [ inputlookup legit_domains.csv 
| fields domain]
| join domain type=outer [
| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site 
| rename "Web.*" as * 
| rex field=site ".*?(?<domain>[^./:]+\.(\S{2,3}
|\S{2,3}.\S{2,3}))$" 
| table dest domain url] 
| table count src dest query answer domain url 
| `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter`

Associated Analytic Story

• Common Phishing Frameworks

How To Implement

You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the legit_domains.csv file shipped with the app.
Splunk>Phantom Playbook Integration
If Splunk>Phantom is also configured in your environment, a Playbook called Lets Encrypt Domain Investigate can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk https://splunkbase.splunk.com/app/3411/, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active.
(Playbook link:https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/).\

Required field

• _time

• DNS.answer

• DNS.dest

• DNS.src

• DNS.query

• host

ATT&CK

ID	Technique	Tactic
T1566.003	Spearphishing via Service	Initial Access

Kill Chain Phase

• Delivery

• Command and Control

Known False Positives

If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains.

Reference

Test Dataset

version: 2

---

Detect Dump LSASS Memory using comsvcs

This search detects the memory of lsass.exe being dumped for offline credential theft attack.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1003.003
• Last Updated: 2020-09-15

details

Search

| from read_ssa_enriched_events() 
| eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)) 
| where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body=create_map(["process_name", process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Credential Dumping

How To Implement

You must be ingesting endpoint data that tracks process activity, including Windows command line logging. You can see how we test this with Event Code 4688 on the attack_range.

Required field

• process_name

• _tenant

• _time

• dest_device_id

• process

ATT&CK

ID	Technique	Tactic
T1003.003	NTDS	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

version: 1

---

Detect Excessive Account Lockouts From Endpoint

This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078.002
• Last Updated: 2020-11-09

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result 
|`drop_dm_object_name("All_Changes")` 
|`drop_dm_object_name("Account_Management")`
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search count > 5 
| `detect_excessive_account_lockouts_from_endpoint_filter`

Associated Analytic Story

• Account Monitoring and Controls

How To Implement

You must ingest your Windows security event logs in the Change datamodel under the nodename is Account_Management, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment.
Splunk>Phantom Playbook Integration
If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk https://splunkbase.splunk.com/app/3411/, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active.
(Playbook Link:https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/).\

Required field

• _time

• All_Changes.user

• nodename

• All_Changes.result

• All_Changes.dest

ATT&CK

ID	Technique	Tactic
T1078.002	Domain Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log

version: 5

---

Detect Excessive User Account Lockouts

This search detects user accounts that have been locked out a relatively high number of times in a short period.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1078.003
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result 
|`drop_dm_object_name("All_Changes")` 
|`drop_dm_object_name("Account_Management")`
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search count > 5 
| `detect_excessive_user_account_lockouts_filter`

Associated Analytic Story

• Account Monitoring and Controls

How To Implement

ou must ingest your Windows security event logs in the Change datamodel under the nodename is Account_Management, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment.

Required field

• _time

• All_Changes.result

• nodename

• All_Changes.user

ATT&CK

ID	Technique	Tactic
T1078.003	Local Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log

version: 3

---

Detect Exchange Web Shell

The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group. Paths include: \HttpProxy\owa\auth\, \inetpub\wwwroot\aspnet_client\, and \HttpProxy\OAB\. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1505.003
• Last Updated: 2021-03-09

details

Search

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| join process_guid, _time [
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path 
| `drop_dm_object_name(Filesystem)` 
| fields _time dest file_create_time file_name file_path process_name process_path process] 
| dedup file_create_time 
| table dest file_create_time, file_name, file_path, process_name 
| `detect_exchange_web_shell_filter`

Associated Analytic Story

• HAFNIUM Group

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node and Filesystem node.

Required field

• _time

• Filesystem.file_path

• Filesystem.process_id

• Filesystem.file_name

• Filesystem.file_hash

• Filesystem.user

ATT&CK

ID	Technique	Tactic
T1505.003	Web Shell	Persistence

Kill Chain Phase

• Exploitation

Known False Positives

The query is structured in a way that action (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.

Reference

• https://raw.githubusercontent.com/Azure/Azure-Sentinel/master/Sample%20Data/Feeds/MSTICIoCs-ExchangeServerVulnerabilitiesDisclosedMarch2021.csv

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_proxylogon.log

version: 2

---

Detect F5 TMUI RCE CVE-2020-5902

This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1190
• Last Updated: 2020-08-02

details

Search

`f5_bigip_rogue` 
| regex _raw="(hsqldb;
|.*\\.\\.;.*)" 
| search `detect_f5_tmui_rce_cve_2020_5902_filter`

Associated Analytic Story

• F5 TMUI RCE CVE-2020-5902

How To Implement

To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;).

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1190	Exploit Public-Facing Application	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/

• https://support.f5.com/csp/article/K52145254

• https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/

Test Dataset

version: 1

---

Detect GCP Storage access from a new IP

This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1530
• Last Updated: 2020-08-10

details

Search

`google_gcp_pubsub_message` 
| multikv 
| rename sc_status_ as status 
| rename cs_object_ as bucket_name 
| rename c_ip_ as remote_ip 
| rename cs_uri_ as request_uri 
| rename cs_method_ as operation 
| search status="\"200\"" 
| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri 
| table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri 
| inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv 
| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri 
| outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv 
| eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) 
| where newIP=1 
| eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") 
| eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") 
| table  first_time last_time bucket_name remote_ip operation request_uri 
| `detect_gcp_storage_access_from_a_new_ip_filter`

Associated Analytic Story

• Suspicious GCP Storage Activities

How To Implement

This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets.

Required field

• _time

• sc_status_

• cs_object_

• c_ip_

• cs_uri_

• cs_method_

ATT&CK

ID	Technique	Tactic
T1530	Data from Cloud Storage Object	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours.

Reference

Test Dataset

version: 1

---

Detect HTML Help Renamed

The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1218.001
• Last Updated: 2021-02-11

details

Search

`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_html_help_renamed_filter`

Associated Analytic Story

• Suspicious Compiled HTML Activity

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used.

Required field

• _time

• EventID

• OriginalFileName

• process_name

• Computer

• User

• parent_process_name

• process_path

• CommandLine

ATT&CK

ID	Technique	Tactic
T1218.001	Compiled HTML File	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed.

Reference

• https://attack.mitre.org/techniques/T1218/001/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md

• https://lolbas-project.github.io/lolbas/Binaries/Hh/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log

version: 1

---

Detect HTML Help Spawn Child Process

The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.001
• Last Updated: 2021-02-11

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_html_help_spawn_child_process_filter`

Associated Analytic Story

• Suspicious Compiled HTML Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.parent_process_name

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1218.001	Compiled HTML File	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed.

Reference

• https://attack.mitre.org/techniques/T1218/001/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md

• https://lolbas-project.github.io/lolbas/Binaries/Hh/

• https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7

• https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log

version: 1

---

Detect HTML Help URL in Command Line

The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.001
• Last Updated: 2021-02-11

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_html_help_url_in_command_line_filter`

Associated Analytic Story

• Suspicious Compiled HTML Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1218.001	Compiled HTML File	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed.

Reference

• https://attack.mitre.org/techniques/T1218/001/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md

• https://lolbas-project.github.io/lolbas/Binaries/Hh/

• https://blog.sevagas.com/?Hacking-around-HTA-files

• https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7

• https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log

version: 1

---

Detect HTML Help Using InfoTech Storage Handlers

The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.001
• Last Updated: 2021-02-11

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_html_help_using_infotech_storage_handlers_filter`

Associated Analytic Story

• Suspicious Compiled HTML Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1218.001	Compiled HTML File	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed.

Reference

• https://attack.mitre.org/techniques/T1218/001/

• https://www.kb.cert.org/vuls/id/851869

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md

• https://lolbas-project.github.io/lolbas/Binaries/Hh/

• https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7

• https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log

version: 1

---

Detect IPv6 Network Infrastructure Threats

By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1200, T1498, T1557.002
• Last Updated: 2020-10-28

details

Search

`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") 
| eval src_interface=src_int_prefix_long+src_int_suffix 
| eval dest_interface=dest_int_prefix_long+dest_int_suffix 
| stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface 
| table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `detect_ipv6_network_infrastructure_threats_filter`

Associated Analytic Story

• Router and Infrastructure Security

How To Implement

This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.

Required field

• _time

• facility

• mnemonic

• src_int_prefix_long

• src_int_suffix

• dest_int_prefix_long

• dest_int_suffix

• src_mac

• src_vlan

• vendor_explanation

• action

ATT&CK

ID	Technique	Tactic
T1200	Hardware Additions	Initial Access
T1498	Network Denial of Service	Impact
T1557.002	ARP Cache Poisoning	Collection, Credential Access

Kill Chain Phase

• Reconnaissance

• Delivery

• Actions on Objectives

Known False Positives

None currently known

Reference

• https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf

• https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html

• https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html

• https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html

• https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html

• https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html

• https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html

• https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html

Test Dataset

version: 1

---

Detect Kerberoasting

This search detects a potential kerberoasting attack via service principal name requests

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1558.003
• Last Updated: 2020-10-21

details

Search

 
| from read_ssa_enriched_events() 
| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id") 
| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" 
| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] 
| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID 
| eval start_time=_time, end_time=_time, body=create_map(["EventCode", EventCode, "ServiceName", ServiceName, "TicketOptions", TicketOptions, "TicketEncryptionType", TicketEncryptionType]), entities="TBD" 
| select start_time, end_time, entities, body 
| into write_null(); 

Associated Analytic Story

• Credential Dumping

How To Implement

The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2

Required field

• service_name

• _time

• event_code

• ticket_encryption_type

• service_id

• ticket_options

ATT&CK

ID	Technique	Tactic
T1558.003	Kerberoasting	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Older systems that support kerberos RC4 by default NetApp may generate false positives

Reference

• Initial ESCU implementation by Jose Hernandez and Patrick Bareiss

Test Dataset

version: 1

---

Detect Large Outbound ICMP Packets

This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1095
• Last Updated: 2018-06-01

details

Search

| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip 
| `drop_dm_object_name("All_Traffic")` 
| search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `detect_large_outbound_icmp_packets_filter`

Associated Analytic Story

• Command and Control

How To Implement

In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named internal to the CIDRs that host the company's assets in the assets_by_cidr.csv lookup file, which is located in $SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model

Required field

• _time

• All_Traffic.action

• All_Traffic.bytes

• All_Traffic.dest_category

• All_Traffic.protocol

• All_Traffic.transport

• All_Traffic.src_ip

• All_Traffic.dest_ip

ATT&CK

ID	Technique	Tactic
T1095	Non-Application Layer Protocol	Command and Control

Kill Chain Phase

• Command and Control

Known False Positives

ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro detect_large_outbound_icmp_packets_filter to adjust the byte threshold or add specific IP addresses to an allow list.

Reference

Test Dataset

version: 2

---

Detect Long DNS TXT Record Response

This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1048.003
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type 
|  `drop_dm_object_name("DNS")` 
| eval anslen=len(answer) 
| search anslen>100 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count 
| table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type"  "Answer Length" Count "First Time" "Last Time" 
| `detect_long_dns_txt_record_response_filter`

Associated Analytic Story

• Suspicious DNS Traffic

• Command and Control

How To Implement

To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol.

Required field

• _time

• DNS.message_type

• DNS.record_type

• DNS.src

• DNS.dest

• DNS.answer

ATT&CK

ID	Technique	Tactic
T1048.003	Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol	Exfiltration

Kill Chain Phase

• Command and Control

Known False Positives

It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives.

Reference

Test Dataset

version: 2

---

Detect MSHTA Url in Command Line

This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.005
• Last Updated: 2021-01-20

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_mshta_url_in_command_line_filter`

Associated Analytic Story

• Suspicious MSHTA Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1218.005	Mshta	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

It is possible legitimate applications may perform this behavior and will need to be filtered.

Reference

• https://github.com/redcanaryco/AtomicTestHarnesses

• https://redcanary.com/blog/introducing-atomictestharnesses/

• https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Mimikatz Using Loaded Images

This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1003.001
• Last Updated: 2019-12-03

details

Search

`sysmon` EventCode=7 
| stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image 
| search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_mimikatz_using_loaded_images_filter`

Associated Analytic Story

• Credential Dumping

• Detect Zerologon Attack

• Cloud Federated Credential Abuse

• DarkSide Ransomware

How To Implement

This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named sysmon. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

Required field

• _time

• EventCode

• ImageLoaded

• ProcessId

• Computer

• Image

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Other tools can import the same DLLs. These tools should be part of a whitelist. False positives may be present with any process that authenticates or uses credentials, PowerShell included. Filter based on parent process.

Reference

• https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Mimikatz Via PowerShell And EventCode 4703

This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1003.001
• Last Updated: 2019-02-27

details

Search

`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe 
| rex field=Message "Enabled Privileges:\s+(?<privs>\w+)\s+Disabled Privileges:" 
| where privs="SeDebugPrivilege" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message 
| rename privs as "Enabled Privilege" 
| rename Process_Name as process 
|  `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_mimikatz_via_powershell_and_eventcode_4703_filter`

Associated Analytic Story

• Cloud Federated Credential Abuse

How To Implement

You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes.

Required field

• _time

• signature_id

• Process_Name

• Message

• dest

• Process_ID

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise.

Reference

Test Dataset

version: 2

---

Detect New Local Admin account

This search looks for newly created accounts that have been elevated to local administrators.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1136.001
• Last Updated: 2020-07-08

details

Search

`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) 
| transaction member_id connected=false maxspan=180m 
| rename member_id as user 
| stats count min(_time) as firstTime max(_time) as lastTime by user dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_new_local_admin_account_filter`

Associated Analytic Story

• DHS Report TA18-074A

• HAFNIUM Group

How To Implement

You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732

Required field

• _time

• EventCode

• Group_Name

• member_id

• dest

ATT&CK

ID	Technique	Tactic
T1136.001	Local Account	Persistence

Kill Chain Phase

• Actions on Objectives

• Command and Control

Known False Positives

The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log

version: 2

---

Detect New Login Attempts to Routers

The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Authentication
• ATT&CK:
• Last Updated: 2017-09-12

details

Search

| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user
| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) 
| where isOutlier=1
| `security_content_ctime(earliest)`
| `security_content_ctime(latest)` 
| `drop_dm_object_name("Authentication")` 
| `detect_new_login_attempts_to_routers_filter`

Associated Analytic Story

• Router and Infrastructure Security

How To Implement

To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure.

Required field

• _time

• Authentication.dest_category

• Authentication.dest

• Authentication.user

Kill Chain Phase

• Actions on Objectives

Known False Positives

Legitimate router connections may appear as new connections

Reference

Test Dataset

version: 1

---

Detect New Open GCP Storage Buckets

This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1530
• Last Updated: 2020-08-05

details

Search

`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions 
| spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action 
| spath output=user path=data.protoPayload.authenticationInfo.principalEmail 
| spath output=location path=data.protoPayload.resourceLocation.currentLocations{} 
| spath output=src path=data.protoPayload.requestMetadata.callerIp 
| spath output=bucketName path=data.protoPayload.resourceName 
| spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role 
| spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member 
| search (member=allUsers AND action=ADD) 
| table  _time, bucketName, src, user, location, action, role, member 
| search `detect_new_open_gcp_storage_buckets_filter`

Associated Analytic Story

• Suspicious GCP Storage Activities

How To Implement

This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).

Required field

• _time

• data.resource.type

• data.protoPayload.methodName

• data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action

• data.protoPayload.authenticationInfo.principalEmail

• data.protoPayload.resourceLocation.currentLocations{}

• data.protoPayload.requestMetadata.callerIp

• data.protoPayload.resourceName

• data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role

• data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member

ATT&CK

ID	Technique	Tactic
T1530	Data from Cloud Storage Object	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group.

Reference

Test Dataset

version: 1

---

Detect New Open S3 Buckets over AWS CLI

This search looks for CloudTrail events where a user has created an open/public S3 bucket over the aws cli.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1530
• Last Updated: 2021-01-12

details

Search

`cloudtrail` eventSource="s3.amazonaws.com" eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") 
| rename requestParameters.bucketName AS bucketName 
| fillnull 
| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_new_open_s3_buckets_over_aws_cli_filter` 

Associated Analytic Story

• Suspicious AWS S3 Activities

How To Implement

Required field

• _time

• eventSource

• eventName

• requestParameters.accessControlList.x-amz-grant-read-acp

• requestParameters.accessControlList.x-amz-grant-write

• requestParameters.accessControlList.x-amz-grant-write-acp

• requestParameters.accessControlList.x-amz-grant-full-control

• requestParameters.bucketName

• userName

• userIdentity.principalId

• userAgent

• bucketName

ATT&CK

ID	Technique	Tactic
T1530	Data from Cloud Storage Object	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json

version: 1

---

Detect New Open S3 buckets

This search looks for CloudTrail events where a user has created an open/public S3 bucket.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1530
• Last Updated: 2021-01-12

details

Search

`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl 
| rex field=_raw "(?<json_field>{.+})" 
| spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} 
| search grantees=* 
| mvexpand grantees 
| spath input=grantees output=uri path=Grantee.URI 
| spath input=grantees output=permission path=Permission 
| search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") 
| search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") 
| rename requestParameters.bucketName AS bucketName 
| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId userAgent uri permission bucketName 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_new_open_s3_buckets_filter` 

Associated Analytic Story

• Suspicious AWS S3 Activities

How To Implement

You must install the AWS App for Splunk.

Required field

• _time

• eventSource

• eventName

• requestParameters.bucketName

• userName

• userIdentity.principalId

• userAgent

• uri

• permission

ATT&CK

ID	Technique	Tactic
T1530	Data from Cloud Storage Object	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json

version: 2

---

Detect Outbound SMB Traffic

This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1071.002
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip 
| `drop_dm_object_name("All_Traffic")` 
| `security_content_ctime(start_time)` 
| `security_content_ctime(end_time)` 
| `detect_outbound_smb_traffic_filter`

Associated Analytic Story

• Hidden Cobra Malware

• DHS Report TA18-074A

• NOBELIUM Group

How To Implement

In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named internal to the CIDRs that host the companys assets in assets_by_cidr.csv lookup file, which is located in $SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model

Required field

• _time

• All_Traffic.action

• All_Traffic.app

• All_Traffic.dest_ip

• All_Traffic.dest_port

• sourcetype

• All_Traffic.dest_category

• All_Traffic.src_ip

ATT&CK

ID	Technique	Tactic
T1071.002	File Transfer Protocols	Command and Control

Kill Chain Phase

• Actions on Objectives

• Command and Control

Known False Positives

It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as internal in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary.

Reference

Test Dataset

version: 3

---

Detect Outlook exe writing a zip file

This search looks for execution of process outlook.exe where the process is writing a .zip file to the disk.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1566.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly`  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename process_id as malicious_id
| rename parent_process_id as outlook_id
| join malicious_id type=inner[
| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name  FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip*   OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by  _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest  
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename process_id as malicious_id
| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] 
| table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name  file_path 
| where file_name != "" 
| `detect_outlook_exe_writing_a_zip_file_filter` 

Associated Analytic Story

• Spearphishing Attachments

How To Implement

You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.

Required field

• _time

• Processes.process_name

• Processes.parent_process_id

• Processes.process_id

• Processes.dest

• Processes.parent_process_name

• Processes.user

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Installation

• Actions on Objectives

Known False Positives

It is not uncommon for outlook to write legitimate zip files to the disk.

Reference

Test Dataset

version: 3

---

Detect Pass the Hash

This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1550.002
• Last Updated: 2020-10-21

details

Search

 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval signature_id=map_get(input_event, "signature_id"), authentication_type=map_get(input_event, "authentication_type"), authentication_method=map_get(input_event, "authentication_method"), origin_device_domain=map_get(input_event, "origin_device_domain"), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null)

| where (authentication_type="3" AND authentication_method="NtLmSsp") OR (authentication_type="9" AND authentication_method="seclogo")

| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body=create_map(["authentication_type", authentication_type, "authentication_method", authentication_method]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Lateral Movement

How To Implement

The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2

Required field

• signature_id

• authentication_type

• _time

• authentication_method

• origin_device_domain

• dest_user_id

• dest_device_id

ATT&CK

ID	Technique	Tactic
T1550.002	Pass the Hash	Defense Evasion, Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate.

Reference

• Initial ESCU implementation by Bhavin Patel and Patrick Bareiss

Test Dataset

version: 1

---

Detect Path Interception By Creation Of program exe

The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1574.009
• Last Updated: 2020-07-03

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| rex field=process "^.*?\\\\(?<service_process>[^\\\\]*\.(?:exe
|bat
|com
|ps1))" 
| eval process_name = lower(process_name) 
| eval service_process = lower(service_process) 
| where process_name != service_process 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_path_interception_by_creation_of_program_exe_filter`

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.parent_process_name

• Processes.user

• Processes.process_name

• Processes.process

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1574.009	Path Interception by Unquoted Path	Defense Evasion, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

unknown

Reference

• https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log

version: 3

---

Detect Port Security Violation

By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1200, T1498, T1557.002
• Last Updated: 2020-10-28

details

Search

`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") 
| eval src_interface=src_int_prefix_long+src_int_suffix 
| stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_port_security_violation_filter`

Associated Analytic Story

• Router and Infrastructure Security

How To Implement

This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.

Required field

• _time

• facility

• mnemonic

• disable_cause

• src_int_prefix_long

• src_int_suffix

• src_mac

• src_vlan

• action

• host

• src_interface

ATT&CK

ID	Technique	Tactic
T1200	Hardware Additions	Initial Access
T1498	Network Denial of Service	Impact
T1557.002	ARP Cache Poisoning	Collection, Credential Access

Kill Chain Phase

• Reconnaissance

• Delivery

• Exploitation

• Actions on Objectives

Known False Positives

This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network.

Reference

Test Dataset

version: 1

---

Detect Prohibited Applications Spawning cmd exe

This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.003
• Last Updated: 2020-11-10

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|search [`prohibited_apps_launching_cmd`] 
| `detect_prohibited_applications_spawning_cmd_exe_filter`

Associated Analytic Story

• Suspicious Command-Line Executions

• Suspicious MSHTA Activity

• Suspicious Zoom Child Processes

• NOBELIUM Group

How To Implement

You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, prohibited_apps_launching_cmd.csv, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1059.003	Windows Command Shell	Execution

Kill Chain Phase

• Exploitation

Known False Positives

There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log

version: 5

---

Detect Prohibited Applications Spawning cmd exe

This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. This is a SPL2 implementation of the rule Detect Prohibited Applications Spawning cmd.exe by @bpatel.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1059
• Last Updated: 2020-7-13

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null)

| where process_name="cmd.exe" 
| rex field=parent_process "(?<field0>[^\\\\]+)$" 
| where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe"

| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body=create_map([ "process_name", process_name, "parent_process_name", parent_process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Suspicious Command-Line Executions

• Suspicious MSHTA Activity

• Suspicious Zoom Child Processes

• Sunburst Malware

How To Implement

You must be ingesting sysmon logs. This search has been modified to process raw sysmon data from attack_range's nxlogs on DSP.

Required field

• process_name

• parent_process_name

• _time

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1059	Command and Scripting Interpreter	Execution

Kill Chain Phase

• Exploitation

Known False Positives

There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate.

Reference

Test Dataset

version: 1

---

Detect PsExec With accepteula Flag

This search looks for events where PsExec.exe is run with the accepteula flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument accepteula within the command line.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1021.002
• Last Updated: 2020-11-10

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest  Processes.parent_process_name 
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_psexec_with_accepteula_flag_filter`

Associated Analytic Story

• SamSam Ransomware

• DHS Report TA18-074A

• HAFNIUM Group

• DarkSide Ransomware

• Lateral Movement

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.dest

• Processes.parent_process_name

ATT&CK

ID	Technique	Tactic
T1021.002	SMB/Windows Admin Shares	Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators can leverage PsExec for accessing remote systems and might pass accepteula as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log

version: 3

---

Detect RClone Command-Line Usage

This analytic identifies commonly used command-line arguments used by rclone.exe to initiate a file transfer. Some arguments were negated as they are specific to the configuration used by adversaries. In particular, an adversary may list the files or directories of the remote file share using ls or lsd, which is not indicative of malicious behavior. During triage, at this stage of a ransomware event, exfiltration is about to occur or has already. Isolate the endpoint and continue investigating by review file modifications and parallel processes.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1020
• Last Updated: 2021-05-13

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*copy*", "*mega*", "*pcloud*", "*ftp*", "*--config*", "*--progress*", "*--no-check-certificate*", "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", "*--multi-thread-streams*")  by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process   Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|  `security_content_ctime(lastTime)` 
| `detect_rclone_command_line_usage_filter`

Associated Analytic Story

• DarkSide Ransomware

• Ransomware

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1020	Automated Exfiltration	Exfiltration

Kill Chain Phase

• Exfiltration

Known False Positives

There is potential for false positives as these arguments may be used by other applications. Filter or tune the analytic as needed.

Reference

• https://redcanary.com/blog/rclone-mega-extortion/

• https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

• https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log

version: 1

---

Detect Rare Executables

This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK:
• Last Updated: 2020-03-16

details

Search

| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name  
| rename Processes.process_name as process 
| rex field=user "(?<user_domain>.*)\\\\(?<user_name>.*)" 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| search [
| tstats count from datamodel=Endpoint.Processes by Processes.process_name 
| rare Processes.process_name limit=30 
| rename Processes.process_name as process
| `filter_rare_process_allow_list`
| table process ] 
| `detect_rare_executables_filter` 

Associated Analytic Story

• Emotet Malware DHS Report TA18-201A

• Unusual Processes

• Cloud Federated Credential Abuse

How To Implement

To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro filter_rare_process_allow_list searches two lookup files for allowed processes. These consist of rare_process_allow_list_default.csv and rare_process_allow_list_local.csv. To add your own processes to the allow list, add them to rare_process_allow_list_local.csv. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.process_name

Kill Chain Phase

• Installation

• Command and Control

• Actions on Objectives

Known False Positives

Some legitimate processes may be only rarely executed in your environment. As these are identified, update rare_process_allow_list_local.csv to filter them out of your search results.

Reference

Test Dataset

version: 5

---

Detect Regasm Spawning a Process

The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.009
• Last Updated: 2021-02-12

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regasm_spawning_a_process_filter`

Associated Analytic Story

• Suspicious Regsvcs Regasm Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.parent_process_name

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1218.009	Regsvcs/Regasm	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

Reference

• https://attack.mitre.org/techniques/T1218/009/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

• https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/

• https://lolbas-project.github.io/lolbas/Binaries/Regasm/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Regasm with Network Connection

The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1218.009
• Last Updated: 2021-02-16

details

Search

`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe 
| rename Computer as dest 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regasm_with_network_connection_filter`

Associated Analytic Story

• Suspicious Regsvcs Regasm Activity

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• dest_ip

• process_name

• Computer

• User

• src_ip

• dest_host

• dest_ip

ATT&CK

ID	Technique	Tactic
T1218.009	Regsvcs/Regasm	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

Reference

• https://attack.mitre.org/techniques/T1218/009/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

• https://lolbas-project.github.io/lolbas/Binaries/Regasm/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Regasm with no Command Line Arguments

The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1218.009
• Last Updated: 2021-02-12

details

Search

`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) 
| regex CommandLine="(regasm\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regasm_with_no_command_line_arguments_filter`

Associated Analytic Story

• Suspicious Regsvcs Regasm Activity

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• process_name

• OriginalFileName

• CommandLine

• dest

• User

• ParentImage

• ParentCommandLine

• process_path

• Computer

ATT&CK

ID	Technique	Tactic
T1218.009	Regsvcs/Regasm	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

Reference

• https://attack.mitre.org/techniques/T1218/009/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

• https://lolbas-project.github.io/lolbas/Binaries/Regasm/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Regsvcs Spawning a Process

The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.009
• Last Updated: 2021-02-12

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regsvcs_spawning_a_process_filter`

Associated Analytic Story

• Suspicious Regsvcs Regasm Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.parent_process_name

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1218.009	Regsvcs/Regasm	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

Reference

• https://attack.mitre.org/techniques/T1218/009/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

• https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Regsvcs with Network Connection

The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1218.009
• Last Updated: 2021-02-16

details

Search

`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe 
| rename Computer as dest 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regsvcs_with_network_connection_filter`

Associated Analytic Story

• Suspicious Regsvcs Regasm Activity

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• dest_ip

• process_name

• Computer

• User

• src_ip

• dest_host

ATT&CK

ID	Technique	Tactic
T1218.009	Regsvcs/Regasm	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

Reference

• https://attack.mitre.org/techniques/T1218/009/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

• https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Regsvcs with No Command Line Arguments

The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1218.009
• Last Updated: 2021-02-12

details

Search

`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) 
| regex CommandLine="(regsvcs\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regsvcs_with_no_command_line_arguments_filter`

Associated Analytic Story

• Suspicious Regsvcs Regasm Activity

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• process_name

• OriginalFileName

• CommandLine

• dest

• User

• ParentImage

• ParentCommandLine

• OriginalFileName

• process_path

• Computer

ATT&CK

ID	Technique	Tactic
T1218.009	Regsvcs/Regasm	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

Reference

• https://attack.mitre.org/techniques/T1218/009/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

• https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Regsvr32 Application Control Bypass

Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack.
Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.010
• Last Updated: 2021-01-28

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_regsvr32_application_control_bypass_filter`

Associated Analytic Story

• Suspicious Regsvr32 Activity

• Cobalt Strike

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1218.010	Regsvr32	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Limited false positives related to third party software registering .DLL's.

Reference

• https://attack.mitre.org/techniques/T1218/010/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md

• https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/

• https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Renamed 7-Zip

The following analytic identifies renamed 7-Zip usage using Sysmon. At this stage of an attack, review parallel processes and file modifications for data that is staged or potentially have been exfiltrated. This analytic utilizes the OriginalFileName to capture the renamed process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1560.001
• Last Updated: 2021-05-19

details

Search

`sysmon` EventID=1 (OriginalFileName=7z*.exe AND process_name!=7z*.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_renamed_7_zip_filter`

Associated Analytic Story

• Collection and Staging

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• dest

• User

• parent_process_name

• process_name

• OriginalFileName

• process_path

• CommandLine

• Product

ATT&CK

ID	Technique	Tactic
T1560.001	Archive via Utility	Collection

Kill Chain Phase

• Exfiltration

Known False Positives

Limited false positives, however this analytic will need to be modified for each environment if Sysmon is not used.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log

version: 1

---

Detect Renamed PSExec

The following analytic identifies renamed instances of PsExec.exe being utilized on an endpoint. Most instances, it is highly probable to capture Psexec.exe or other SysInternal utility usage with the command-line argument of -accepteula. In this instance, we are using OriginalFileName from Sysmon to identify PsExec usage. During triage, validate this is the legitimate version of PsExec by review the PE metadata. In addition, review parallel processes for further suspicious behavior.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1569.002
• Last Updated: 2021-05-19

details

Search

`sysmon` EventID=1 (OriginalFileName=psexec.c process_name!=psexec.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine Product 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_renamed_psexec_filter`

Associated Analytic Story

• SamSam Ransomware

• DHS Report TA18-074A

• HAFNIUM Group

• DarkSide Ransomware

• Lateral Movement

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.

Required field

• _time

• dest

• User

• parent_process_name

• process_name

• OriginalFileName

• process_path

• CommandLine

• Product

ATT&CK

ID	Technique	Tactic
T1569.002	Service Execution	Execution

Kill Chain Phase

• Exploitation

• Lateral Movement

• Execution

Known False Positives

Limited false positives should be present. It is possible some third party applications may use older versions of PsExec, filter as needed.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1569.002/T1569.002.yaml

• https://redcanary.com/blog/threat-hunting-psexec-lateral-movement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Renamed RClone

The following analytic identifies the usage of rclone.exe, renamed, being used to exfiltrate data to a remote destination. RClone has been used by multiple ransomware groups to exfiltrate data. In many instances, it will be downloaded from the legitimate site and executed accordingly. During triage, isolate the endpoint and begin to review parallel processes for additional behavior. At this stage, the adversary may have staged data to be exfiltrated.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1020
• Last Updated: 2021-05-13

details

Search

`sysmon` EventID=1 OriginalFileName=rclone.exe NOT process_name=rclone.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_renamed_rclone_filter`

Associated Analytic Story

• DarkSide Ransomware

• Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• OriginalFileName

• process_name

• process_path

• CommandLine

• dest

ATT&CK

ID	Technique	Tactic
T1020	Automated Exfiltration	Exfiltration

Kill Chain Phase

• Exfiltration

Known False Positives

False positives should be limited as this analytic identifies renamed instances of rclone.exe. Filter as needed if there is a legitimate business use case.

Reference

• https://redcanary.com/blog/rclone-mega-extortion/

• https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

• https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log

version: 1

---

Detect Renamed WinRAR

The following analtyic identifies renamed instances of WinRAR.exe. In most cases, it is not common for WinRAR to be used renamed, however it is common to be installed by a third party application and executed from a non-standard path. In this instance, we are using OriginalFileName from Sysmon to determine if the process is WinRAR. During triage, validate additional metadata from the binary that this is WinRAR. Review parallel processes and file modifications.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1560.001
• Last Updated: 2021-05-19

details

Search

`sysmon` EventID=1 (Product=WinRAR OR OriginalFileName=WinRAR.exe) process_name!=rar.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine Product 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_renamed_winrar_filter`

Associated Analytic Story

• Collection and Staging

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Modify query for specific EDR products as needed.

Required field

• _time

• dest

• User

• parent_process_name

• process_name

• OriginalFileName

• process_path

• CommandLine

• Product

ATT&CK

ID	Technique	Tactic
T1560.001	Archive via Utility	Collection

Kill Chain Phase

• Exploitation

• Exfiltration

Known False Positives

Unknown. It is possible third party applications use renamed instances of WinRAR.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log

version: 1

---

Detect Rogue DHCP Server

By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack).

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1200, T1498, T1557
• Last Updated: 2020-08-11

details

Search

`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" 
| stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)`
| `detect_rogue_dhcp_server_filter`

Associated Analytic Story

• Router and Infrastructure Security

How To Implement

This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.

Required field

• _time

• facility

• mnemonic

• message_type

• src_mac

• host

ATT&CK

ID	Technique	Tactic
T1200	Hardware Additions	Initial Access
T1498	Network Denial of Service	Impact
T1557	Man-in-the-Middle	Collection, Credential Access

Kill Chain Phase

• Reconnaissance

• Delivery

• Actions on Objectives

Known False Positives

This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface.

Reference

Test Dataset

version: 1

---

Detect Rundll32 Application Control Bypass - advpack

The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.011
• Last Updated: 2021-02-04

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_rundll32_application_control_bypass___advpack_filter`

Associated Analytic Story

• Suspicious Rundll32 Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive.

Reference

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32

• https://lolbas-project.github.io/lolbas/Libraries/Advpack/

• https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Rundll32 Application Control Bypass - setupapi

The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.011
• Last Updated: 2021-02-04

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_rundll32_application_control_bypass___setupapi_filter`

Associated Analytic Story

• Suspicious Rundll32 Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, some legitimate applications may use setupapi triggering a false positive.

Reference

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32

• https://lolbas-project.github.io/lolbas/Libraries/Setupapi/

• https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Rundll32 Application Control Bypass - syssetup

The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.011
• Last Updated: 2021-02-04

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_rundll32_application_control_bypass___syssetup_filter`

Associated Analytic Story

• Suspicious Rundll32 Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive.

Reference

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32

• https://lolbas-project.github.io/lolbas/Libraries/Syssetup/

• https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log

version: 1

---

Detect Rundll32 Inline HTA Execution

The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.005
• Last Updated: 2021-01-20

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_rundll32_inline_hta_execution_filter`

Associated Analytic Story

• Suspicious MSHTA Activity

• NOBELIUM Group

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.user

• Processes.dest

• Processes.parent_process_name

• Processes.parent_process

ATT&CK

ID	Technique	Tactic
T1218.005	Mshta	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

Reference

• https://github.com/redcanaryco/AtomicTestHarnesses

• https://redcanary.com/blog/introducing-atomictestharnesses/

• https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log

version: 1

---

Detect S3 access from a new IP

This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1530
• Last Updated: 2018-06-28

details

Search

`aws_s3_accesslogs` http_status=200  [search `aws_s3_accesslogs` http_status=200 
| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip 
| inputlookup append=t previously_seen_S3_access_from_remote_ip.csv 
| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip 
| outputlookup previously_seen_S3_access_from_remote_ip.csv 
| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newIP=1 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| table bucket_name remote_ip]
| iplocation remote_ip 
|rename remote_ip as src_ip 
| table _time bucket_name src_ip City Country operation request_uri 
| `detect_s3_access_from_a_new_ip_filter`

Associated Analytic Story

• Suspicious AWS S3 Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names.

Required field

• _time

• http_status

• bucket_name

• remote_ip

ATT&CK

ID	Technique	Tactic
T1530	Data from Cloud Storage Object	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour

Reference

Test Dataset

version: 1

---

Detect SNICat SNI Exfiltration

This search looks for commands that the SNICat tool uses in the TLS SNI field.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1041
• Last Updated: 2020-10-21

details

Search

`zeek_ssl` 
| rex field=server_name "(?<snicat>(LIST
|LS
|SIZE
|LD
|CB
|CD
|EX
|ALIVE
|EXIT
|WHERE
|finito)-[A-Za-z0-9]{16}\.)" 
| stats count by src_ip dest_ip server_name snicat 
| where count>0 
| table src_ip dest_ip server_name snicat 
| `detect_snicat_sni_exfiltration_filter`

Associated Analytic Story

• Data Exfiltration

How To Implement

You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place.

Required field

• _time

• server_name

• src_ip

• dest_ip

ATT&CK

ID	Technique	Tactic
T1041	Exfiltration Over C2 Channel	Exfiltration

Kill Chain Phase

• Actions on Objectives

Known False Positives

Unknown

Reference

• https://www.mnemonic.no/blog/introducing-snicat/

• https://github.com/mnemonic-no/SNIcat

• https://attack.mitre.org/techniques/T1041/

Test Dataset

version: 1

---

Detect SharpHound Command-Line Arguments

The following analytic identifies common command-line arguments used by SharpHound -collectionMethod and invoke-bloodhound. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1087.002, T1087.001, T1482, T1069.002, T1069.001
• Last Updated: 2021-06-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_sharphound_command_line_arguments_filter`

Associated Analytic Story

• Discovery Techniques

• Ransomware

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1087.002	Domain Account	Discovery
T1087.001	Local Account	Discovery
T1482	Domain Trust Discovery	Discovery
T1069.002	Domain Groups	Discovery
T1069.001	Local Groups	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

False positives should be limited as the arguments used are specific to SharpHound. Filter as needed or add more command-line arguments as needed.

Reference

• https://attack.mitre.org/software/S0521/

• https://thedfirreport.com/?s=bloodhound

• https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

• https://github.com/BloodHoundAD/SharpHound3

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log

version: 1

---

Detect SharpHound File Modifications

SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. -randomizefilenames and -encryptzip are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename 20210601181553_BloodHound.zip. SharpHound creates multiple temp files following the same pattern 20210601182121_computers.json, domains.json, gpos.json, ous.json and users.json. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the .ps1 ingestor will be PowerShell.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1087.002, T1087.001, T1482, T1069.002, T1069.001
• Last Updated: 2021-05-27

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_sharphound_file_modifications_filter`

Associated Analytic Story

• Discovery Techniques

• Ransomware

How To Implement

To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the Endpoint datamodel in the Filesystem node.

Required field

• _time

• file_path

• dest

• file_name

• process_id

• file_create_time

ATT&CK

ID	Technique	Tactic
T1087.002	Domain Account	Discovery
T1087.001	Local Account	Discovery
T1482	Domain Trust Discovery	Discovery
T1069.002	Domain Groups	Discovery
T1069.001	Local Groups	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.

Reference

• https://attack.mitre.org/software/S0521/

• https://thedfirreport.com/?s=bloodhound

• https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

• https://github.com/BloodHoundAD/SharpHound3

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log

version: 1

---

Detect SharpHound Usage

The following analytic identifies SharpHound binary usage by using the OriginalFileName from Sysmon. In addition to renaming the PE, other coverage is available to detect command-line arguments. This particular analytic only looks for the OriginalFileName of SharpHound.exe. It is possible older instances of SharpHound.exe have different original filenames. Dependent upon the operator, the code may be re-compiled and the attributes removed or changed to anything else. During triage, review the metadata of the binary in question. Review parallel processes for suspicious behavior. Identify the source of this binary.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1087.002, T1087.001, T1482, T1069.002, T1069.001
• Last Updated: 2021-05-27

details

Search

`sysmon` EventID=1 (OriginalFileName=SharpHound.exe process_name!=sharphound.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine Product 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_sharphound_usage_filter`

Associated Analytic Story

• Discovery Techniques

• Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• dest

• User

• parent_process_name

• process_name

• OriginalFileName

• process_path

• CommandLine

• Product

ATT&CK

ID	Technique	Tactic
T1087.002	Domain Account	Discovery
T1087.001	Local Account	Discovery
T1482	Domain Trust Discovery	Discovery
T1069.002	Domain Groups	Discovery
T1069.001	Local Groups	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

False positives should be limited as this is specific to a file attribute not used by anything else. Filter as needed.

Reference

• https://attack.mitre.org/software/S0521/

• https://thedfirreport.com/?s=bloodhound

• https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

• https://github.com/BloodHoundAD/SharpHound3

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log

version: 1

---

Detect Software Download To Network Device

Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1542.005
• Last Updated: 2020-10-28

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
| `drop_dm_object_name("All_Traffic")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_software_download_to_network_device_filter`

Associated Analytic Story

• Router and Infrastructure Security

How To Implement

This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory.

Required field

• _time

• All_Traffic.transport

• All_Traffic.dest_port

• All_Traffic.dest_category

• All_Traffic.src_category

• All_Traffic.src

• All_Traffic.dest

ATT&CK

ID	Technique	Tactic
T1542.005	TFTP Boot	Defense Evasion, Persistence

Kill Chain Phase

• Delivery

Known False Positives

This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices.

Reference

Test Dataset

version: 1

---

Detect Spike in AWS API Activity

This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall 
| spath output=arn path=userIdentity.arn 
| stats count as apiCalls by arn 
| inputlookup api_call_by_user_baseline append=t 
| fields - latestCount 
| stats values(*) as * by arn 
| rename apiCalls as latestCount 
| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 
| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) 
| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup api_call_by_user_baseline 
| eval dataPointThreshold = 15, deviationThreshold = 3 
| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) 
| where isSpike=1 
| rename arn as userIdentity.arn 
| table userIdentity.arn] 
| spath output=user userIdentity.arn 
| stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user 
| `detect_spike_in_aws_api_activity_filter`

Associated Analytic Story

• AWS User Monitoring

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify dataPointThreshold and deviationThreshold to better fit your environment. The dataPointThreshold variable is the minimum number of data points required to have a statistically significant amount of data to determine. The deviationThreshold variable is the number of standard deviations away from the mean that the value must be to be considered a spike.
This search produces fields (eventName,numberOfApiCalls,uniqueApisCalled) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n1. Label: AWS Event Name, Field: eventName\

1. \
2. Label: Number of API Calls, Field: numberOfApiCalls\
3. \
4. Label: Unique API Calls, Field: uniqueApisCalled
   Detailed documentation on how to create a new field within Incident Review may be found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

Required field

• _time

• eventType

• userIdentity.arn

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Reference

Test Dataset

version: 2

---

Detect Spike in AWS Security Hub Alerts for EC2 Instance

This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2021-01-26

details

Search

`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance 
| bucket span=4h _time 
| stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest 
| eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev 
| eval threshold_value = 3 
| eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) 
| search isOutlier=1 
| table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg 
| `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter`

Associated Analytic Story

• AWS Security Hub Alerts

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.

Required field

• _time

• Resources{}.Type

• Title

• Types{}

• vendor_account

• vendor_region

• severity

• dest

Kill Chain Phase

Known False Positives

None

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json

version: 3

---

Detect Spike in AWS Security Hub Alerts for User

This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2021-01-26

details

Search

`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser 
| rename findings{}.Resources{}.Id as user 
| bucket span=4h _time 
| stats count AS alerts by _time user 
| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev 
| eval threshold_value = 2 
| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) 
| search isOutlier=1 
| table _time user alerts 
|`detect_spike_in_aws_security_hub_alerts_for_user_filter`

Associated Analytic Story

• AWS Security Hub Alerts

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.

Required field

• _time

• findings{}.Resources{}.Type

• indings{}.Resources{}.Id

• user

Kill Chain Phase

Known False Positives

None

Reference

Test Dataset

version: 3

---

Detect Spike in Network ACL Activity

This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1562.007
• Last Updated: 2018-05-21

details

Search

`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` 
| spath output=arn path=userIdentity.arn 
| stats count as apiCalls by arn 
| inputlookup network_acl_activity_baseline append=t 
| fields - latestCount 
| stats values(*) as * by arn 
| rename apiCalls as latestCount 
| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 
| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) 
| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup network_acl_activity_baseline 
| eval dataPointThreshold = 15, deviationThreshold = 3 
| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) 
| where isSpike=1 
| rename arn as userIdentity.arn 
| table userIdentity.arn] 
| spath output=user userIdentity.arn 
| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user 
| `detect_spike_in_network_acl_activity_filter`

Associated Analytic Story

• AWS Network ACL Activity

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify dataPointThreshold and deviationThreshold to better fit your environment. The dataPointThreshold variable is the minimum number of data points required to have a statistically significant amount of data to determine. The deviationThreshold variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro network_acl_events.

Required field

• _time

• userIdentity.arn

ATT&CK

ID	Technique	Tactic
T1562.007	Disable or Modify Cloud Firewall	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

The false-positive rate may vary based on the values ofdataPointThreshold and deviationThreshold. Please modify this according the your environment.

Reference

Test Dataset

version: 1

---

Detect Spike in S3 Bucket deletion

This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1530
• Last Updated: 2018-11-27

details

Search

`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket 
| spath output=arn path=userIdentity.arn 
| stats count as apiCalls by arn 
| inputlookup s3_deletion_baseline append=t 
| fields - latestCount 
| stats values(*) as * by arn 
| rename apiCalls as latestCount 
| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 
| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) 
| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup s3_deletion_baseline 
| eval dataPointThreshold = 15, deviationThreshold = 3 
| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) 
| where isSpike=1 
| rename arn as userIdentity.arn 
| table userIdentity.arn] 
| spath output=user userIdentity.arn 
| spath output=bucketName path=requestParameters.bucketName 
| stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user 
| `detect_spike_in_s3_bucket_deletion_filter`

Associated Analytic Story

• Suspicious AWS S3 Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify dataPointThreshold and deviationThreshold to better fit your environment. The dataPointThreshold variable is the minimum number of data points required to have a statistically significant amount of data to determine. The deviationThreshold variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity.

Required field

• _time

• eventName

• userIdentity.arn

ATT&CK

ID	Technique	Tactic
T1530	Data from Cloud Storage Object	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

Based on the values ofdataPointThreshold and deviationThreshold, the false positive rate may vary. Please modify this according the your environment.

Reference

Test Dataset

version: 1

---

Detect Spike in Security Group Activity

This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2018-04-18

details

Search

`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` 
| spath output=arn path=userIdentity.arn 
| stats count as apiCalls by arn 
| inputlookup security_group_activity_baseline append=t 
| fields - latestCount 
| stats values(*) as * by arn 
| rename apiCalls as latestCount 
| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 
| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) 
| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup security_group_activity_baseline 
| eval dataPointThreshold = 15, deviationThreshold = 3 
| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) 
| where isSpike=1 
| rename arn as userIdentity.arn 
| table userIdentity.arn] 
| spath output=user userIdentity.arn 
| stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user 
| `detect_spike_in_security_group_activity_filter`

Associated Analytic Story

• AWS User Monitoring

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify dataPointThreshold and deviationThreshold to better fit your environment. The dataPointThreshold variable is the minimum number of data points required to have a statistically significant amount of data to determine. The deviationThreshold variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro security_group_api_calls.

Required field

• _time

• serIdentity.arn

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Based on the values ofdataPointThreshold and deviationThreshold, the false positive rate may vary. Please modify this according the your environment.

Reference

Test Dataset

version: 1

---

Detect Spike in blocked Outbound Traffic from your AWS

This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2018-05-07

details

Search

`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  [search  `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  
| stats count as numberOfBlockedConnections by src_ip 
| inputlookup baseline_blocked_outbound_connections append=t 
| fields - latestCount 
| stats values(*) as * by src_ip 
| rename numberOfBlockedConnections as latestCount 
| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 
| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) 
| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) 
| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections 
| outputlookup baseline_blocked_outbound_connections 
| eval dataPointThreshold = 5, deviationThreshold = 3 
| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) 
| where isSpike=1 
| table src_ip] 
| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip 
| `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`

Associated Analytic Story

• AWS Network ACL Activity

• Suspicious AWS Traffic

• Command and Control

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify dataPointThreshold and deviationThreshold to better fit your environment. The dataPointThreshold variable is the number of data points required to meet the definition of "spike." The deviationThreshold variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections.

Required field

• _time

• action

• src_ip

• dest_ip

Kill Chain Phase

• Actions on Objectives

• Command and Control

Known False Positives

The false-positive rate may vary based on the values ofdataPointThreshold and deviationThreshold. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections.

Reference

Test Dataset

version: 1

---

Detect Traffic Mirroring

Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1200, T1498, T1020.001
• Last Updated: 2020-10-28

details

Search

`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") 
| stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `detect_traffic_mirroring_filter`

Associated Analytic Story

• Router and Infrastructure Security

How To Implement

This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring.

Required field

• _time

• facility

• mnemonic

• host

ATT&CK

ID	Technique	Tactic
T1200	Hardware Additions	Initial Access
T1498	Network Denial of Service	Impact
T1020.001	Traffic Duplication	Exfiltration

Kill Chain Phase

• Delivery

• Actions on Objectives

Known False Positives

This search will return false positives for any legitimate traffic captures by network administrators.

Reference

Test Dataset

version: 1

---

Detect USB device insertion

The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change_Analysis
• ATT&CK:
• Last Updated: 2017-11-27

details

Search

| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest 
| `drop_dm_object_name("All_Changes")`
| `security_content_ctime(earliest)`
| `security_content_ctime(latest)`  
| `detect_usb_device_insertion_filter`

Associated Analytic Story

• Data Protection

How To Implement

To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework.

Required field

• _time

• All_Changes.result

• All_Changes.result_id

• All_Changes.src_priority

• All_Changes.dest

Kill Chain Phase

• Installation

• Actions on Objectives

Known False Positives

Legitimate USB activity will also be detected. Please verify and investigate as appropriate.

Reference

Test Dataset

version: 1

---

Detect Unauthorized Assets by MAC address

By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Sessions
• ATT&CK:
• Last Updated: 2017-09-13

details

Search

| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac 
| dedup All_Sessions.dest_mac
| `drop_dm_object_name("Network_Sessions")`
|`drop_dm_object_name("All_Sessions")` 
| search NOT [
| inputlookup asset_lookup_by_str 
|rename mac as dest_mac 
| fields + dest_mac] 
| `detect_unauthorized_assets_by_mac_address_filter`

Associated Analytic Story

• Asset Tracking

How To Implement

This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated.

Required field

• _time

• All_Sessions.signature

• All_Sessions.src_ip

• All_Sessions.dest_mac

Kill Chain Phase

• Reconnaissance

• Delivery

• Actions on Objectives

Known False Positives

This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information.

Reference

Test Dataset

version: 1

---

Detect Use of cmd exe to Launch Script Interpreters

This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.003
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`

Associated Analytic Story

• Emotet Malware DHS Report TA18-201A

• Suspicious Command-Line Executions

How To Implement

To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process

• Processes.parent_process_name

• Processes.process_name

• Processes.parent_process

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1059.003	Windows Command Shell	Execution

Kill Chain Phase

• Exploitation

Known False Positives

Some legitimate applications may exhibit this behavior.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log

version: 4

---

Detect Windows DNS SIGRed via Splunk Stream

This search detects SIGRed via Splunk Stream.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1203
• Last Updated: 2020-07-28

details

Search

`stream_dns` 
| spath "query_type{}" 
| search "query_type{}" IN (SIG,KEY) 
| spath protocol_stack 
| search protocol_stack="ip:tcp:dns" 
| append [search `stream_tcp` bytes_out>65000] 
| `detect_windows_dns_sigred_via_splunk_stream_filter` 
| stats count by flow_id 
| where count>1 
| fields - count

Associated Analytic Story

• Windows DNS SIGRed CVE-2020-1350

How To Implement

You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1203	Exploitation for Client Execution	Execution

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/

Test Dataset

version: 1

---

Detect Windows DNS SIGRed via Zeek

This search detects SIGRed via Zeek DNS and Zeek Conn data.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1203
• Last Updated: 2020-07-28

details

Search

| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id 
| rename DNS.flow_id as flow_id 
| append [
| tstats  `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id 
| rename All_Traffic.flow_id as flow_id] 
| `detect_windows_dns_sigred_via_zeek_filter` 
| stats count by flow_id 
| where count>1 
| fields - count 

Associated Analytic Story

• Windows DNS SIGRed CVE-2020-1350

How To Implement

You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search.

Required field

• _time

• DNS.query_type

• DNS.flow_id

• All_Traffic.bytes_in

• All_Traffic.flow_id

ATT&CK

ID	Technique	Tactic
T1203	Exploitation for Client Execution	Execution

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/

Test Dataset

version: 1

---

Detect Zerologon via Zeek

This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1190
• Last Updated: 2020-09-15

details

Search

`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) 
| bin span=5m _time 
| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip 
| search opscount=3 authcount>4 passcount>0 
| search `detect_zerologon_via_zeek_filter`

Associated Analytic Story

• Detect Zerologon Attack

How To Implement

You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field.

Required field

• _time

• operation

ATT&CK

ID	Technique	Tactic
T1190	Exploit Public-Facing Application	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://www.secura.com/blog/zero-logon

• https://github.com/SecuraBV/CVE-2020-1472

• https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472

Test Dataset

version: 1

---

Detect attackers scanning for vulnerable JBoss servers

This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Web
• ATT&CK: T1082
• Last Updated: 2017-09-23

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_attackers_scanning_for_vulnerable_jboss_servers_filter`

Associated Analytic Story

• JBoss Vulnerability

• SamSam Ransomware

How To Implement

You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model.

Required field

• _time

• Web.http_method

• Web.url

• Web.src

• Web.dest

ATT&CK

ID	Technique	Tactic
T1082	System Information Discovery	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths.

Reference

Test Dataset

version: 1

---

Detect hosts connecting to dynamic domain providers

Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1189
• Last Updated: 2021-01-14

details

Search

| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host 
| `drop_dm_object_name("DNS")` 
| `security_content_ctime(firstTime)` 
| `dynamic_dns_providers` 
| `detect_hosts_connecting_to_dynamic_domain_providers_filter`

Associated Analytic Story

• Data Protection

• Prohibited Traffic Allowed or Protocol Mismatch

• DNS Hijacking

• Suspicious DNS Traffic

• Dynamic DNS

• Command and Control

How To Implement

First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the Network_Resolution data model. This search also leverages a lookup file, dynamic_dns_providers_default.csv, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of dynamic_dns_providers_local.csv.
This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n1. Label: DNS Query, Field: query\

1. \
2. Label: DNS Answer, Field: answer\
3. \
4. Label: IsDynamicDNS, Field: isDynDNS
   Detailed documentation on how to create a new field within Incident Review may be found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

Required field

• _time

• DNS.answer

• DNS.query

• host

ATT&CK

ID	Technique	Tactic
T1189	Drive-by Compromise	Initial Access

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log

version: 3

---

Detect malicious requests to exploit JBoss servers

This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Web
• ATT&CK:
• Last Updated: 2017-09-23

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest 
| search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table src, dest_ip, http_method, url, firstTime, lastTime 
| `detect_malicious_requests_to_exploit_jboss_servers_filter`

Associated Analytic Story

• JBoss Vulnerability

• SamSam Ransomware

How To Implement

You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model

Required field

• _time

• Web.http_method

• Web.url

• Web.url_length

• Web.src

• Web.dest

Kill Chain Phase

• Delivery

Known False Positives

No known false positives for this detection.

Reference

Test Dataset

version: 1

---

Detect mshta inline hta execution

The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.005
• Last Updated: 2021-01-20

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_mshta_inline_hta_execution_filter`

Associated Analytic Story

• Suspicious MSHTA Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.user

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1218.005	Mshta	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

Reference

• https://github.com/redcanaryco/AtomicTestHarnesses

• https://redcanary.com/blog/introducing-atomictestharnesses/

• https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log

version: 5

---

Detect mshta renamed

The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1218.005
• Last Updated: 2021-01-20

details

Search

`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_mshta_renamed_filter`

Associated Analytic Story

• Suspicious MSHTA Activity

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• OriginalFileName

• process_name

• Computer

• User

• parent_process_name

• process_path

• CommandLine

ATT&CK

ID	Technique	Tactic
T1218.005	Mshta	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive.

Reference

• https://github.com/redcanaryco/AtomicTestHarnesses

• https://redcanary.com/blog/introducing-atomictestharnesses/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log

version: 1

---

Detect new API calls from user roles

This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is AssumedRole.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2018-04-16

details

Search

`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success  userIdentity.type=AssumedRole 
| stats earliest(_time) as earliest latest(_time) as latest by userName eventName 
|  inputlookup append=t previously_seen_api_calls_from_user_roles 
| stats min(earliest) as earliest, max(latest) as latest by userName eventName 
| outputlookup previously_seen_api_calls_from_user_roles
| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) 
| where newApiCallfromUserRole=1 
| `security_content_ctime(earliest)` 
| `security_content_ctime(latest)` 
| table eventName userName]  
|rename userName as user
| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user 
| `security_content_ctime(earliest)` 
| `security_content_ctime(latest)` 
| `detect_new_api_calls_from_user_roles_filter`

Associated Analytic Story

• AWS User Monitoring

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles.

Required field

• _time

• eventType

• errorCode

• userIdentity.type

• userName

• eventName

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger.

Reference

Test Dataset

version: 1

---

Detect new user AWS Console Login

This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` eventName=ConsoleLogin 
| rename userIdentity.arn as user 
| stats earliest(_time) as firstTime latest(_time) as lastTime by user 
| inputlookup append=t previously_seen_users_console_logins_cloudtrail  
| stats min(firstTime) as firstTime max(lastTime) as lastTime by user 
| eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)`
| where userStatus ="First Time Logging into AWS Console"  
| `detect_new_user_aws_console_login_filter`

Associated Analytic Story

• Suspicious AWS Login Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.

Required field

• _time

• eventName

• userIdentity.arn

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.

Reference

Test Dataset

version: 2

---

Detect processes used for System Network Configuration Discovery

This search looks for fast execution of processes used for system network configuration discovery on the endpoint.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1016
• Last Updated: 2020-11-10

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| search `system_network_configuration_discovery_tools` 
| transaction dest connected=false maxpause=5m 
|where eventcount>=5 
| table firstTime lastTime dest user process_name process parent_process eventcount 
| `detect_processes_used_for_system_network_configuration_discovery_filter`

Associated Analytic Story

• Unusual Processes

How To Implement

You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.dest

• Processes.process_name

• Processes.user

ATT&CK

ID	Technique	Tactic
T1016	System Network Configuration Discovery	Discovery

Kill Chain Phase

• Installation

• Command and Control

• Actions on Objectives

Known False Positives

It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log

version: 2

---

Detect web traffic to dynamic domain providers

This search looks for web connections to dynamic DNS providers.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Web
• ATT&CK: T1071.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `dynamic_dns_web_traffic` 
| `detect_web_traffic_to_dynamic_domain_providers_filter`

Associated Analytic Story

• Dynamic DNS

How To Implement

This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, dynamic_dns_providers_default.csv, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.
This search produces fields (isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n1. Label: IsDynamicDNS, Field: isDynDNS
Detailed documentation on how to create a new field within Incident Review may be found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details Deprecated because duplicate.

Required field

• _time

• Web.url

• Web.status

• Web.src

• Web.dest

ATT&CK

ID	Technique	Tactic
T1071.001	Web Protocols	Command and Control

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate.

Reference

Test Dataset

version: 2

---

Detection of DNS Tunnels

This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1048.003
• Last Updated: 2017-09-18

details

Search

| tstats `security_content_summariesonly` dc("DNS.query") as count  from datamodel=Network_Resolution  where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*"   ) by "DNS.src","DNS.query" 
| rename "DNS.src" as src  "DNS.query" as message 
| eval length=len(message) 
| stats sum(length) as length by src 
| append [ tstats `security_content_summariesonly` dc("DNS.answer") as count  from datamodel=Network_Resolution  where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*"   ) by "DNS.src","DNS.answer" 
| rename "DNS.src" as src  "DNS.answer" as message 
| eval message=if(message=="unknown","", message) 
| eval length=len(message) 
| stats sum(length) as length by src ] 
| stats sum(length) as length by src 
| where length > 10000 
| `detection_of_dns_tunnels_filter`

Associated Analytic Story

• Data Protection

• Suspicious DNS Traffic

• Command and Control

How To Implement

To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the cim_corporate_web_domain_search() macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue.

Required field

• _time

• DNS.query

• DNS.message_type

• DNS.src_category

• DNS.src

ATT&CK

ID	Technique	Tactic
T1048.003	Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol	Exfiltration

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment.

Reference

Test Dataset

version: 2

---

Detection of tools built by NirSoft

This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1072
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `detection_of_tools_built_by_nirsoft_filter`

Associated Analytic Story

• Emotet Malware DHS Report TA18-201A

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

ATT&CK

ID	Technique	Tactic
T1072	Software Deployment Tools	Execution, Lateral Movement

Kill Chain Phase

• Installation

• Actions on Objectives

Known False Positives

While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose.

Reference

Test Dataset

version: 3

---

Disable Registry Tool

This search is to identifies modification of registry to disable the regedit or registry tools of windows operating system. Since registry tool is a swiss knife in analyzing registry, malware such as RAT or trojan Spy disable this application to prevent the removal of their registry entry such as persistence, file less components and defense evasion.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_registry_tool_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin may disable this application for non technical user.

Reference

• https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disable Show Hidden Files

The following search is to idetifies a modification in registry to prevent the user seeing all the files with hidden attributes. This event or techniques are known on some worm and trojan spy malware that will drop hidden files on the infected machine.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1564.001, T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\HideFileExt" Registry.registry_value_name = "DWORD (0x00000001)") OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\ShowSuperHidden" Registry.registry_value_name = "DWORD (0x00000000)") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_show_hidden_files_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_nam

ATT&CK

ID	Technique	Tactic
T1564.001	Hidden Files and Directories	Defense Evasion
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://www.sophos.com/en-us/threat-center/threat-analyses/viruses-and-spyware/W32~Tiotua-P/detailed-analysis.aspx

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disable Windows App Hotkeys

This analytic detects a suspicious registry modification to disable Windows hotkey (shortcut keys) for native Windows applications. This technique is commonly used to disable certain or several Windows applications like taskmgr.exe and cmd.exe. This technique is used to impair the analyst in analyzing and removing the attacker implant in compromised systems.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-05-05

details

Search

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Windows NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_name = "HotKey Disabled" AND Registry.registry_key_name = "Debugger" by Registry.dest Registry.user Registry.registry_value_name 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `disable_windows_app_hotkeys_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as CarbonBlack or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.registry_value_name

• Registry.dest Registry.user

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log

version: 1

---

Disable Windows Behavior Monitoring

This search is to identifies a modification in registry to disable the windows denfender real time behavior monitoring. This event or technique is commonly seen in RAT, bot, or Trojan to disable AV to evade detections.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableBehaviorMonitoring" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableOnAccessProtection" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableScanOnRealtimeEnable" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_windows_behavior_monitoring_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin or user may choose to disable this windows features.

Reference

• https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disable Windows SmartScreen Protection

The following search identifies a modification of registry to disable the smartscreen protection of windows machine. This is windows feature provide an early warning system against website that might engage in phishing attack or malware distribution. This modification are seen in RAT malware to cover their tracks upon downloading other of its component or other payload.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled"  Registry.registry_value_name = "Off" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_windows_smartscreen_protection_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_nam

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin or user may choose to disable this windows features.

Reference

• https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disabling CMD Application

this search is to identify modification in registry to disable cmd prompt application. This technique is commonly seen in RAT, Trojan or WORM to prevent triaging or deleting there samples through cmd application which is one of the tool of analyst to traverse on directory and files.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disabling_cmd_application_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin may disable this application for non technical user.

Reference

• https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disabling ControlPanel

this search is to identify registry modification to disable control panel window. This technique is commonly seen in malware to prevent their artifacts , persistence removed on the infected machine.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_controlpanel_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin may disable this application for non technical user.

Reference

• https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disabling Firewall with Netsh

This search is to identifies suspicious firewall disabling using netsh application. this technique is commonly seen in malware that tries to communicate or download its component or other payload to its C2 server.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" (Processes.process= "*off*" OR  Processes.process= "*disable*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_firewall_with_netsh_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin may disable firewall during testing or fixing network problem.

Reference

• https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.htm

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disabling FolderOptions Windows Feature

This search is to identify registry modification to disable folder options feature of windows to show hidden files, file extension and etc. This technique used by malware in combination if disabling show hidden files feature to hide their files and also to hide the file extension to lure the user base on file icons or fake file extensions.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_folderoptions_windows_feature_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin may disable this application for non technical user.

Reference

• https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disabling Net User Account

This analytic will identify a suspicious command-line that disables a user account using the net.exe utility native to Windows. This technique may used by the adversaries to interrupt availability of such users to do their malicious act.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1531
• Last Updated: 2021-05-04

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND Processes.process="*user*" AND Processes.process="*/active:no*" by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_net_user_account_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed net.exe/net1.exe may be used.

Required field

• _time

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.parent_process_name

• Processes.process_id

• Processes.parent_process

ATT&CK

ID	Technique	Tactic
T1531	Account Access Removal	Impact

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Disabling NoRun Windows App

This search is to identify modification of registry to disable run application in window start menu. this application is known to be a helpful shortcut to windows OS user to run known application and also to execute some reg or batch script. This technique is used malware to make cleaning of its infection more harder by preventing known application run easily through run shortcut.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_norun_windows_app_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin may disable this application for non technical user.

Reference

• https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry

• https://blog.malwarebytes.com/detections/pum-optional-norun/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disabling Remote User Account Control

The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC).

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1548.002
• Last Updated: 2020-11-18

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action 
| `drop_dm_object_name(Registry)` 
| `disabling_remote_user_account_control_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

• Suspicious Windows Registry Activities

How To Implement

To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.

Required field

• _time

• Registry.registry_path

• Registry.registry_value_name

• Registry.dest

• Registry.registry_key_name

• Registry.user

• Registry.action

ATT&CK

ID	Technique	Tactic
T1548.002	Bypass User Account Control	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log

version: 4

---

Disabling SystemRestore In Registry

The following search identifies the modification of registry related in disabling the system restore of a machine. This event or behavior are seen in some RAT malware to make the restore of the infected machine difficult and keep their infection on the box.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableConfig" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disabling_systemrestore_in_registry_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

in some cases admin can disable systemrestore on a machine.

Reference

• https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Disabling Task Manager

This search is to identifies modification of registry to disable the task manager of windows operating system. this event or technique are commonly seen in malware such as RAT, Trojan, TrojanSpy or worm to prevent the user to terminate their process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-31

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_task_manager_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

admin may disable this application for non technical user.

Reference

• https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry

• https://blog.talosintelligence.com/2020/05/threat-roundup-0424-0501.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log

version: 1

---

Download Files Using Telegram

The following analytic will identify a suspicious download by the Telegram application on a Windows system. This behavior was identified on a honeypot where the adversary gained access, installed Telegram and followed through with downloading different network scanners (port, bruteforcer, masscan) to the system and later used to mapped the whole network and further move laterally.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1105
• Last Updated: 2021-05-06

details

Search

`sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier" 
|stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode Image process_id TargetFilename Hash 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `download_files_using_telegram_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name and TargetFilename from your endpoints or Events that monitor filestream events which is happened when process download something. (EventCode 15) If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Computer

• EventCode

• Image

• process_id

• TargetFilename

• Hash

ATT&CK

ID	Technique	Tactic
T1105	Ingress Tool Transfer	Command and Control

Kill Chain Phase

• Exploitation

Known False Positives

normal download of file in telegram app. (if it was a common app in network)

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/minergate/windows-sysmon.log

version: 1

---

Dump LSASS via comsvcs DLL

Detect the usage of comsvcs.dll for dumping the lsass process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.001
• Last Updated: 2020-02-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dump_lsass_via_comsvcs_dll_filter`

Associated Analytic Story

• Credential Dumping

• Suspicious Rundll32 Activity

• HAFNIUM Group

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/

• https://twitter.com/SBousseaden/status/1167417096374050817

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log

version: 1

---

Dump LSASS via procdump

Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.001
• Last Updated: 2021-02-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe OR Processes.process_name=procdump64.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dump_lsass_via_procdump_filter`

Associated Analytic Story

• Credential Dumping

• HAFNIUM Group

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://attack.mitre.org/techniques/T1003/001/

• https://docs.microsoft.com/en-us/sysinternals/downloads/procdump

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log

version: 1

---

Dump LSASS via procdump Rename

Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1003.001
• Last Updated: 2021-02-01

details

Search

`sysmon` OriginalFileName=procdump  process_name!=procdump*.exe  EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* 
| rename Computer as dest 
|  stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dump_lsass_via_procdump_rename_filter`

Associated Analytic Story

• Credential Dumping

• HAFNIUM Group

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• OriginalFileName

• process_name

• EventID

• CommandLine

• Computer

• parent_process_name

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://attack.mitre.org/techniques/T1003/001/

• https://docs.microsoft.com/en-us/sysinternals/downloads/procdump

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log

version: 1

---

EC2 Instance Modified With Previously Unseen User

This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success 
| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn 
| rename userIdentity.arn as arn 
| inputlookup append=t previously_seen_ec2_modifications_by_user 
| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn 
| outputlookup previously_seen_ec2_modifications_by_user 
| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newUser=1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename arn as userIdentity.arn 
| table userIdentity.arn] 
| spath output=dest responseElements.instancesSet.items{}.instanceId 
| spath output=user userIdentity.arn 
| table _time, user, dest 
| `ec2_instance_modified_with_previously_unseen_user_filter`

Associated Analytic Story

• Unusual AWS EC2 Modifications

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro ec2_modification_api_calls.

Required field

• _time

• errorCode

• userIdentity.arn

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.

Reference

Test Dataset

version: 3

---

EC2 Instance Started In Previously Unseen Region

This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1535
• Last Updated: 2018-02-23

details

Search

`cloudtrail` earliest=-1h StartInstances 
| stats earliest(_time) as earliest latest(_time) as latest by awsRegion 
| inputlookup append=t previously_seen_aws_regions.csv 
| stats min(earliest) as earliest max(latest) as latest by awsRegion 
| outputlookup previously_seen_aws_regions.csv 
| eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") 
| `security_content_ctime(earliest)` 
| `security_content_ctime(latest)` 
| where regionStatus="Instance Started in a New Region" 
| `ec2_instance_started_in_previously_unseen_region_filter`

Associated Analytic Story

• AWS Cryptomining

• Suspicious AWS EC2 Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel.

Required field

• _time

• awsRegion

ATT&CK

ID	Technique	Tactic
T1535	Unused/Unsupported Cloud Regions	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate.

Reference

Test Dataset

version: 1

---

EC2 Instance Started With Previously Unseen AMI

This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2018-03-12

details

Search

`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success 
| stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId 
| rename requestParameters.instancesSet.items{}.imageId as amiID 
| inputlookup append=t previously_seen_ec2_amis.csv 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID 
| outputlookup previously_seen_ec2_amis.csv 
| eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| where newAMI=1 
| rename amiID as requestParameters.instancesSet.items{}.imageId 
| table requestParameters.instancesSet.items{}.imageId] 
| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID 
| table firstTime, lastTime, arn, amiID, dest, instanceType 
| `ec2_instance_started_with_previously_unseen_ami_filter`

Associated Analytic Story

• AWS Cryptomining

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs.

Required field

• _time

• eventName

• errorCode

• requestParameters.instancesSet.items{}.imageId

Kill Chain Phase

Known False Positives

After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user.

Reference

Test Dataset

version: 1

---

EC2 Instance Started With Previously Unseen Instance Type

This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-02-07

details

Search

`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success 
| fillnull value="m1.small" requestParameters.instanceType 
| stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType 
| rename requestParameters.instanceType as instanceType 
| inputlookup append=t previously_seen_ec2_instance_types.csv 
| stats min(earliest) as earliest max(latest) as latest by instanceType 
| outputlookup previously_seen_ec2_instance_types.csv 
| eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) 
| `security_content_ctime(earliest)` 
| `security_content_ctime(latest)` 
| where newType=1 
| rename instanceType as requestParameters.instanceType 
| table requestParameters.instanceType] 
| spath output=user userIdentity.arn 
| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest 
| table _time, user, dest, instanceType 
| `ec2_instance_started_with_previously_unseen_instance_type_filter`

Associated Analytic Story

• AWS Cryptomining

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types.

Required field

• _time

• eventName

• errorCode

• requestParameters.instanceType

Kill Chain Phase

Known False Positives

It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type.

Reference

Test Dataset

version: 2

---

EC2 Instance Started With Previously Unseen User

This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.004
• Last Updated: 2020-07-21

details

Search

`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success 
| stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn 
| rename userIdentity.arn as arn 
| inputlookup append=t previously_seen_ec2_launches_by_user.csv 
| stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn 
| outputlookup previously_seen_ec2_launches_by_user.csv 
| eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newUser=1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename arn as userIdentity.arn 
| table userIdentity.arn] 
| rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user 
| table _time, user, dest, instanceType 
| `ec2_instance_started_with_previously_unseen_user_filter`

Associated Analytic Story

• AWS Cryptomining

• Suspicious AWS EC2 Activities

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs.

Required field

• _time

• eventName

• errorCode

• userIdentity.arn

ATT&CK

ID	Technique	Tactic
T1078.004	Cloud Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior.

Reference

Test Dataset

version: 2

---

Email Attachments With Lots Of Spaces

Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Email
• ATT&CK:
• Last Updated: 2017-09-19

details

Search

| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Email")` 
| eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) 
| search space_ratio >= 0.1 
|  rex field=recipient_address "(?<recipient_user>.*)@" 
| `email_attachments_with_lots_of_spaces_filter`

Associated Analytic Story

• Emotet Malware DHS Report TA18-201A

• Suspicious Emails

How To Implement

You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment.
Splunk Phantom Playbook Integration
If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk https://splunkbase.splunk.com/app/3411/ and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.

Required field

• _time

• All_Email.recipient

• All_Email.file_name

• All_Email.src_user

• All_Email.file_name

• All_Email.message_id

Kill Chain Phase

• Delivery

Known False Positives

None at this time

Reference

Test Dataset

version: 2

---

Email files written outside of the Outlook directory

The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1114.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*"  Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest 
| `drop_dm_object_name("Filesystem")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `email_files_written_outside_of_the_outlook_directory_filter` 

Associated Analytic Story

• Collection and Staging

How To Implement

To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.

Required field

• _time

• Filesystem.file_path

• Filesystem.file_name

• Filesystem.action

• Filesystem.process_id

• Filesystem.dest

ATT&CK

ID	Technique	Tactic
T1114.001	Local Email Collection	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search.

Reference

Test Dataset

version: 3

---

Email servers sending high volume traffic to hosts

This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1114.002
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d 
| `drop_dm_object_name("All_Traffic")` 
| eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out 
| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip 
| eval minimum_data_samples = 4, deviation_threshold = 3 
| where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") 
| eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) 
| table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average 
| `email_servers_sending_high_volume_traffic_to_hosts_filter`

Associated Analytic Story

• Collection and Staging

• HAFNIUM Group

How To Implement

This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.

Required field

• _time

• All_Traffic.bytes_out

• All_Traffic.src_category

• All_Traffic.dest_ip

ATT&CK

ID	Technique	Tactic
T1114.002	Remote Email Collection	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.

Reference

Test Dataset

version: 2

---

Enable RDP In Other Port Number

This search is to detect a modification to registry to enable rdp to a machine with different port number. This technique was seen in some atttacker tries to do lateral movement and remote access to a compromised machine to gain control of it.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1021
• Last Updated: 2021-05-19

details

Search

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*HKLM\\SYSTEM\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp*" Registry.registry_key_name = "PortNumber" by Registry.dest Registry.user Registry.registry_value_name 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `enable_rdp_in_other_port_number_filter`

Associated Analytic Story

• Prohibited Traffic Allowed or Protocol Mismatch

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.

Required field

• _time

• Registry.registry_path

• Registry.dest

• Registry.user

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1021	Remote Services	Lateral Movement

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://www.mvps.net/docs/how-to-secure-remote-desktop-rdp/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log

version: 1

---

Enumerate Users Local Group Using Telegram

This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1087
• Last Updated: 2021-05-06

details

Search

`wineventlog_security` EventCode=4798  Process_Name = "*\\telegram.exe" 
| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode Process_Name  Process_ID Account_Name Account_Domain Logon_ID Security_ID Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `enumerate_users_local_group_using_telegram_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and filter known instances of process like logonUI used in your environment.

Required field

• _time

• ComputerName

• EventCode

• Process_Name

• Process_ID

• Account_Name

• Account_Domain

• Logon_ID

• Security_ID

• Message

ATT&CK

ID	Technique	Tactic
T1087	Account Discovery	Discovery

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4798

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/minergate/windows-security.log

version: 1

---

Eventvwr UAC Bypass

The following search identifies Eventvwr bypass by identifying the registry modification into a specific path that eventvwr.msc looks to (but is not valid) upon execution. A successful attack will include a suspicious command to be executed upon eventvwr.msc loading. Upon triage, review the parallel processes that have executed. Identify any additional registry modifications on the endpoint that may look suspicious. Remediate as necessary.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1548.002
• Last Updated: 2021-03-01

details

Search

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where  Registry.registry_path="*mscfile\\shell\\open\\command\\*"  by Registry.user, Registry.dest , Registry.registry_value_name
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `eventvwr_uac_bypass_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.user

• Registry.dest

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1548.002	Bypass User Account Control	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

• Privilege Escalation

Known False Positives

Some false positives may be present and will need to be filtered.

Reference

• https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md

• https://attack.mitre.org/techniques/T1548/002

• https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log

version: 1

---

Excel Spawning PowerShell

The following detection identifies Microsoft Excel spawning PowerShell. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path C:\Program Files\Microsoft Office\root\Office16 (version will vary). PowerShell spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.002
• Last Updated: 2021-04-12

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe")  by Processes.parent_process Processes.process_name Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `excel_spawning_powershell_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1003.002	Security Account Manager	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

False positives should be limited, but if any are present, filter as needed.

Reference

• https://redcanary.com/threat-detection-report/techniques/powershell/

• https://attack.mitre.org/techniques/T1566/001/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log

version: 1

---

Excel Spawning Windows Script Host

The following detection identifies Microsoft Excel spawning Windows Script Host - cscript.exe or wscript.exe. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path C:\Program Files\Microsoft Office\root\Office16 (version will vary). cscript.exe or wscript.exe default location is c:\windows\system32\ or c:windows\syswow64. cscript.exeorwscript.exe` spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.002
• Last Updated: 2021-04-12

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe", "wscript.exe")  by Processes.parent_process Processes.process_name Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `excel_spawning_windows_script_host_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1003.002	Security Account Manager	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

False positives should be limited, but if any are present, filter as needed. In some instances, cscript.exe is used for legitimate business practices.

Reference

• https://app.any.run/tasks/8ecfbc29-03d0-421c-a5bf-3905d29192a2/

• https://attack.mitre.org/techniques/T1566/001/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log

version: 1

---

Excessive Attempt To Disable Services

This analytic will identify suspicious series of command-line to disable several services. This technique is seen where the adversary attempts to disable security app services or other malware services to complete the objective on the compromised system.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1489
• Last Updated: 2021-05-04

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where   Processes.process_name = "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m 
| where count >=5 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_attempt_to_disable_services_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed sc.exe may be used.

Required field

• _time

• Processes.process

• Processes.process_id

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1489	Service Stop	Impact

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Excessive DNS Failures

This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1071.004
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"
| `drop_dm_object_name("DNS")`
| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain
| where isnull(domain)
| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank
| where isnull(rank)
| stats sum(count) as count mode(queries) as queries by src
| `get_asset(src)`
| where count>50 
| `excessive_dns_failures_filter`

Associated Analytic Story

• Suspicious DNS Traffic

• Command and Control

How To Implement

To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.

Required field

• _time

• DNS.query

• DNS.reply_code

• DNS.src

ATT&CK

ID	Technique	Tactic
T1071.004	DNS	Command and Control

Kill Chain Phase

• Command and Control

Known False Positives

It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment.

Reference

Test Dataset

version: 2

---

Excessive Service Stop Attempt

This analytic identifies suspicious series of attempt to kill multiple services on a system using either net.exe or sc.exe. This technique is use by adversaries to terminate security services or other related services to continue there objective and evade detections.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1489
• Last Updated: 2021-05-04

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "net.exe" OR  Processes.process_name = "sc.exe" OR  Processes.process_name = "net1.exe" AND Processes.process="*stop*" OR Processes.process="*/delete*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m 
| where count >=5 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_service_stop_attempt_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed sc.exe may be used.

Required field

• _time

• Processes.process

• Processes.process_id

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1489	Service Stop	Impact

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Excessive Usage Of Cacls App

The following analytic identifies excessive usage of cacls.exe, xcacls.exe or icacls.exe application to change file or folder permission. This behavior is commonly seen where the adversary attempts to impair some users from deleting or accessing its malware components or artifact from the compromised system.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1222
• Last Updated: 2021-05-07

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id  values(Processes.process_name) as process_name count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "XCACLS.exe" by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m 
| where count >=10 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_cacls_app_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.process_id

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1222	File and Directory Permissions Modification	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Administrators or administrative scripts may use this application. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Excessive Usage Of Net App

This analytic identifies excessive usage of net.exe or net1.exe within a bucket of time (1 minute). This behavior was seen in a Monero incident where the adversary attempts to create many users, delete and disable users as part of its malicious behavior.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1531
• Last Updated: 2021-05-06

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "net.exe" OR Processes.process_name = "net1.exe" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m 
| where count >=10 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_net_app_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed net.exe may be used.

Required field

• _time

• Processes.process

• Processes.process_id

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1531	Account Access Removal	Impact

Kill Chain Phase

• Exploitation

Known False Positives

unknown. Filter as needed. Modify the time span as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Excessive Usage Of Taskkill

This analytic identifies excessive usage of taskkill.exe application. This application is commonly used by adversaries to evade detections by killing security product processes or even other processes to evade detection.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-05-04

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe"  by Processes.parent_process_name Processes.process_name Processes.dest Processes.user _time span=1m 
| where count >=10 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_taskkill_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed taskkill.exe may be used.

Required field

• _time

• Processes.parent_process_name

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.process

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Unknown. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Excessive Usage of NSLOOKUP App

this search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type (TXT, A, AAAA) that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1048
• Last Updated: 2021-04-21

details

Search

`sysmon` EventCode = 1 process_name = "nslookup.exe" 
|  bucket _time span=15m 
| stats count as numNsLookup by Computer, _time 
|  eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots by Computer 
|  eval upperThreshold=(avgNsLookup + stdNsLookup *3) 
|  eval isOutlier=if(avgNsLookup > 20 and avgNsLookup >= upperThreshold, 1, 0) 
|  search isOutlier=1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_nslookup_app_filter`

Associated Analytic Story

• Suspicious DNS Traffic

• Dynamic DNS

• Command and Control

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of nslookup.exe may be used.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1048	Exfiltration Over Alternative Protocol	Exfiltration

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://www.fireeye.com/blog/threat-research/2017/03/fin7_spear_phishing.html

• https://www.varonis.com/blog/dns-tunneling/

• https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/windows-sysmon.log

version: 1

---

Excessive number of taskhost processes

This detection targets behaviors observed in post exploit kits like Meterpreter and Koadic that are run in memory. We have observed that these tools must invoke an excessive number of taskhost.exe and taskhostex.exe processes to complete various actions (discovery, lateral movement, etc.). It is extremely uncommon in the course of normal operations to see so many distinct taskhost and taskhostex processes running concurrently in a short time frame.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1033
• Last Updated: 2021-06-07

details

Search

| tstats `security_content_summariesonly` values(Processes.process_id) as process_ids  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "taskhost.exe" OR Processes.process_name = "taskhostex.exe" BY Processes.dest Processes.process_name _time span=1h 
| `drop_dm_object_name(Processes)` 
| eval pid_count=mvcount(process_ids) 
| eval taskhost_count_=if(process_name == "taskhost.exe", pid_count, 0) 
| eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, 0) 
| stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count by _time, dest, firstTime, lastTime 
| where taskhost_count > 10 and taskhostex_count > 10 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_number_of_taskhost_processes_filter`

Associated Analytic Story

• Meterpreter

How To Implement

To successfully implement this search you need to be ingesting events related to processes on the endpoints that include the name of the process and process id into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_id

• Processes.process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1033	System Owner/User Discovery	Discovery

Kill Chain Phase

• Exploitation

Known False Positives

Administrators, administrative actions or certain applications may run many instances of taskhost and taskhostex concurrently. Filter as needed.

Reference

• https://attack.mitre.org/software/S0250/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/meterpreter/taskhost_processes/logExcessiveTaskHost.log

version: 1

---

Executables Or Script Creation In Suspicious Path

This analytic will identify suspicious executable or scripts (known file extensions) in list of suspicious file path in Windows. This technique is used by adversaries to evade detection. The suspicious file path are known paths used in the wild and are not common to have executable or scripts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1036
• Last Updated: 2021-05-06

details

Search

|tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = *.exe OR Filesystem.file_name = *.dll OR Filesystem.file_name = *.sys OR Filesystem.file_name = *.com OR Filesystem.file_name = *.vbs OR Filesystem.file_name = *.vbe OR Filesystem.file_name = *.js OR Filesystem.file_name = *.ps1 OR Filesystem.file_name = *.bat OR Filesystem.file_name = *.cmd OR Filesystem.file_name = *.pif) AND ( Filesystem.file_path = *\\windows\\fonts\\* OR Filesystem.file_path = *\\windows\\temp\\* OR Filesystem.file_path = *\\users\\public\\* OR Filesystem.file_path = *\\windows\\debug\\* OR Filesystem.file_path = *\\Users\\Administrator\\Music\\* OR Filesystem.file_path = *\\Windows\\servicing\\* OR Filesystem.file_path = *\\Users\\Default\\* OR Filesystem.file_path = *Recycle.bin* OR Filesystem.file_path = *\\Windows\\Media\\* OR Filesystem.file_path = *\\Windows\\repair\\* OR Filesystem.file_path = *\\AppData\\Local\\Temp*) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `executables_or_script_creation_in_suspicious_path_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the Endpoint datamodel in the Filesystem node.

Required field

• _time

• Filesystem.file_path

• Filesystem.file_create_time

• Filesystem.process_id

• Filesystem.file_name

• Filesystem.user

ATT&CK

ID	Technique	Tactic
T1036	Masquerading	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Administrators may allow creation of script or exe in the paths specified. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Execution of File With Spaces Before Extension

This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1036.003
• Last Updated: 2020-11-19

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*     .*" by Processes.dest Processes.user Processes.process Processes.process_name 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| `execution_of_file_with_spaces_before_extension_filter`

Associated Analytic Story

• Windows File Extension and Association Abuse

• Masquerading - Rename System Utilities

How To Implement

To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process_path

• Processes.process

• Processes.dest

• Processes.user

• Processes.process_name

ATT&CK

ID	Technique	Tactic
T1036.003	Rename System Utilities	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

Test Dataset

version: 3

---

Execution of File with Multiple Extensions

This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1036.003
• Last Updated: 2020-11-18

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| `execution_of_file_with_multiple_extensions_filter`

Associated Analytic Story

• Windows File Extension and Association Abuse

• Masquerading - Rename System Utilities

How To Implement

To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node.

Required field

• _time

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

ATT&CK

ID	Technique	Tactic
T1036.003	Rename System Utilities	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log

version: 3

---

Extended Period Without Successful Netbackup Backups

This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2017-09-12

details

Search

`netbackup` MESSAGE="Disk/Partition backup completed successfully." 
| stats latest(_time) as latestTime by COMPUTERNAME 
| `security_content_ctime(latestTime)` 
| rename COMPUTERNAME as dest 
| eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) 
| search isOutlier=1 
| table latestTime, dest 
| `extended_period_without_successful_netbackup_backups_filter`

Associated Analytic Story

• Monitor Backup Solution

How To Implement

To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days.

Required field

• _time

• MESSAGE

• COMPUTERNAME

Kill Chain Phase

Known False Positives

None identified

Reference

Test Dataset

version: 1

---

Extract SAM from Registry

The following analytic identifies the use of reg.exe exporting Windows Registry hives containing credentials. Adversaries may use this technique to export registry hives for offline credential access attacks. Typically found executed from a untrusted process or script. Upon execution, a file will be written to disk.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.002
• Last Updated: 2021-05-12

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=reg.exe (Processes.process=*save* OR Processes.process=*export*) AND (Processes.process=*sam* OR Processes.process=*system* OR Processes.process=*security*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `extract_sam_from_registry_filter`

Associated Analytic Story

• DarkSide Ransomware

• Credential Dumping

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1003.002	Security Account Manager	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

It is possible some agent based products will generate false positives. Filter as needed.

Reference

• https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.002/T1003.002.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log

version: 1

---

File with Samsam Extension

The search looks for file writes with extensions consistent with a SamSam ransomware attack.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK:
• Last Updated: 2018-12-14

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)`
| rex field=file_name "(?<file_extension>\.[^\.]+)$" 
| search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml 
| `file_with_samsam_extension_filter`

Associated Analytic Story

• SamSam Ransomware

How To Implement

You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

• Filesystem.user

• Filesystem.dest

• Filesystem.file_path

• Filesystem.file_name

Kill Chain Phase

• Installation

Known False Positives

Because these extensions are not typically used in normal operations, you should investigate all results.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log

version: 1

---

First Time Seen Child Process of Zoom

This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1068
• Last Updated: 2020-05-20

details

Search

| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest 
| `drop_dm_object_name(Processes)` 
| lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen 
| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") 
| `security_content_ctime(firstTime)` 
| table firstTime dest, process_id, process_name, parent_process_id, parent_process_name 
|`first_time_seen_child_process_of_zoom_filter`

Associated Analytic Story

• Suspicious Zoom Child Processes

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search Previously Seen Zoom Child Processes - Initial to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search Previously Seen Zoom Child Processes - Update to keep this table up to date and to age out old child processes. Please update the previously_seen_zoom_child_processes_window macro to adjust the time window.

Required field

• _time

• Processes.parent_process_name

• Processes.parent_process_id

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.process_id

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/zoom_child_process/windows-sysmon.log

version: 1

---

First Time Seen Running Windows Service

This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1569.002
• Last Updated: 2020-07-21

details

Search

`wineventlog_system` EventCode=7036 
| rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" 
| where state="running" 
| lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen 
| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) 
| table _time dest service 
| `first_time_seen_running_windows_service_filter`

Associated Analytic Story

• Windows Service Abuse

• Orangeworm Attack Group

• NOBELIUM Group

How To Implement

While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search Previously Seen Running Windows Services - Initial to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search Previously Seen Running Windows Services - Update to keep this table up to date and to age out old Windows Services. Please update the previously_seen_windows_services_window macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.

Required field

• _time

• EventCode

• Message

• dest

ATT&CK

ID	Technique	Tactic
T1569.002	Service Execution	Execution

Kill Chain Phase

• Installation

• Actions on Objectives

Known False Positives

A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process.

Reference

Test Dataset

version: 4

---

First time seen command line argument

This search looks for command-line arguments that use a /c parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule First time seen command line argument by @bpatel.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1059, T1117, T1202
• Last Updated: 2021-2-1

details

Search

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s
|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s
|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N") 
| where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true 
| select cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id 
| first_time_event input_columns=["cmd_line_norm"] 
| where first_time_cmd_line_norm 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body=create_map(["cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Unusual Processes

How To Implement

You must be populating the endpoint data model for SSA and specifically the process_name and the process fields

Required field

• process_name

• _time

• dest_device_id

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1059	Command and Scripting Interpreter	Execution
T1202	Indirect Command Execution	Defense Evasion

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the first_time_seen_cmd_line_filter macro to exclude legitimate parent_process_name

Reference

Test Dataset

version: 2

---

First time seen command line argument

This search looks for command-line arguments that use a /c parameter to execute a command that has not previously been seen.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001, T1059.003
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search [
| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process 
| `drop_dm_object_name(Processes)` 
| inputlookup append=t previously_seen_cmd_line_arguments 
| stats min(firstTime) as firstTime, max(lastTime) as lastTime by process 
| outputlookup previously_seen_cmd_line_arguments 
| eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newCmdLineArgument=1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table process] 
| `first_time_seen_command_line_argument_filter` 

Associated Analytic Story

• DHS Report TA18-074A

• Suspicious Command-Line Executions

• Orangeworm Attack Group

• Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

• Hidden Cobra Malware

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called previously_seen_cmd_line_arguments.csv—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the relative_time evaluation function.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution
T1059.003	Windows Command Shell	Execution

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the first_time_seen_cmd_line_filter macro to exclude legitimate parent_process_name

Reference

Test Dataset

version: 5

---

FodHelper UAC Bypass

Fodhelper.exe has a known UAC bypass as it attempts to look for specific registry keys upon execution, that do not exist. Therefore, an attacker can write its malicious commands in these registry keys to be executed by fodhelper.exe with the highest privilege. \

1. HKCU:\Software\Classes\ms-settings\shell\open\command\
2. HKCU:\Software\Classes\ms-settings\shell\open\command\DelegateExecute\
3. HKCU:\Software\Classes\ms-settings\shell\open\command\(default)
   Upon triage, fodhelper.exe will have a child process and read access will occur on the registry keys. Isolate the endpoint and review parallel processes for additional behavior.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1112, T1548.002
• Last Updated: 2021-03-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=fodhelper.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `fodhelper_uac_bypass_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.parent_process_name

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1112	Modify Registry	Defense Evasion
T1548.002	Bypass User Account Control	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

• Privilege Escalation

Known False Positives

Limited to no false positives are expected.

Reference

• https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md

• https://github.com/gushmazuko/WinBypass/blob/master/FodhelperBypass.ps1

• https://attack.mitre.org/techniques/T1548/002

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log

version: 1

---

GCP Detect accounts with high risk roles by project

This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2020-10-09

details

Search

`google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor 
| table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} 
| `gcp_detect_accounts_with_high_risk_roles_by_project_filter`

Associated Analytic Story

• GCP Cross Account Activity

How To Implement

You must install splunk GCP add-on. This search works with gcp:pubsub:message logs

Required field

• _time

• data.protoPayload.request.policy.bindings{}.role

• data.resource.type data.protoPayload.authenticationInfo.principalEmail

• data.protoPayload.authorizationInfo{}.permission

• data.protoPayload.authorizationInfo{}.resource

• data.protoPayload.response.bindings{}.role

• data.protoPayload.response.bindings{}.members{}

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Lateral Movement

Known False Positives

Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization

Reference

• https://github.com/dxa4481/gcploit

• https://www.youtube.com/watch?v=Ml09R38jpok

• https://cloud.google.com/iam/docs/understanding-roles

Test Dataset

version: 1

---

GCP Detect gcploit framework

This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2020-10-08

details

Search

`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s 
| table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent 
| `gcp_detect_gcploit_framework_filter`

Associated Analytic Story

• GCP Cross Account Activity

How To Implement

You must install splunk GCP add-on. This search works with gcp:pubsub:message logs

Required field

• _time

• data.protoPayload.request.function.timeout

• src

• src_user

• data.resource.labels.project_id

• data.protoPayload.request.function.serviceAccountEmail

• data.protoPayload.authorizationInfo{}.permission

• data.protoPayload.request.location

• http_user_agent

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Lateral Movement

Known False Positives

Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects

Reference

• https://github.com/dxa4481/gcploit

• https://www.youtube.com/watch?v=Ml09R38jpok

Test Dataset

version: 1

---

GCP Detect high risk permissions by resource and account

This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2020-10-09

details

Search

`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create 
|table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id 
| `gcp_detect_high_risk_permissions_by_resource_and_account_filter`

Associated Analytic Story

• GCP Cross Account Activity

How To Implement

You must install splunk GCP add-on. This search works with gcp:pubsub:message logs

Required field

• _time

• data.protoPayload.authorizationInfo{}.permission

• data.protoPayload.requestMetadata.callerIp

• data.protoPayload.authenticationInfo.principalEmail

• data.protoPayload.authorizationInfo{}.permission

• data.protoPayload.response.bindings{}.members{}

• data.resource.labels.project_id

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Lateral Movement

Known False Positives

High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives.

Reference

• https://github.com/dxa4481/gcploit

• https://www.youtube.com/watch?v=Ml09R38jpok

• https://cloud.google.com/iam/docs/permissions-reference

Test Dataset

version: 1

---

GCP GCR container uploaded

This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1525
• Last Updated: 2020-02-20

details

Search

|tstats count min(_time) as firstTime max(_time) as lastTime  FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path 
| `drop_dm_object_name("Storage")`  
| `gcp_gcr_container_uploaded_filter` 

Associated Analytic Story

• Container Implantation Monitoring and Investigation

How To Implement

You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the container_implant_gcp_detection_filter macro to filter out the false positives.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1525	Implant Internal Image	Persistence

Kill Chain Phase

Known False Positives

Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path.

Reference

Test Dataset

version: 1

---

GCP Kubernetes cluster pod scan detection

This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1526
• Last Updated: 2020-07-17

details

Search

`google_gcp_pubsub_message` category=kube-audit 
|spath input=properties.log 
|search responseStatus.code=401 
|table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod 
| `gcp_kubernetes_cluster_pod_scan_detection_filter`

Associated Analytic Story

• Kubernetes Scanning Activity

How To Implement

You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.

Required field

• _time

• category

• responseStatus.code

• sourceIPs{}

• userAgent

• verb

• requestURI

• responseStatus.reason

• properties.pod

ATT&CK

ID	Technique	Tactic
T1526	Cloud Service Discovery	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context.

Reference

Test Dataset

version: 1

---

GCP Kubernetes cluster scan detection

This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1526
• Last Updated: 2020-04-15

details

Search

`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" 
| rename data.protoPayload.requestMetadata.callerIp as src_ip 
| stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name 
| rename data.resource.labels.cluster_name as cluster_name
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)`  
| `gcp_kubernetes_cluster_scan_detection_filter` 

Associated Analytic Story

• Kubernetes Scanning Activity

How To Implement

You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1526	Cloud Service Discovery	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context.

Reference

Test Dataset

version: 1

---

GPUpdate with no Command Line Arguments with Network

The following analytic identifies gpupdate.exe with no command line arguments and with a network connection. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1055
• Last Updated: 2021-04-19

details

Search

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=gpupdate.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| regex process="(gpupdate\.exe.{0,4}$)" 
| join  process_id [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
| `drop_dm_object_name(Ports)` 
| rename  dest as connection_to_CNC] 
| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port 
| `gpupdate_with_no_command_line_arguments_with_network_filter`

Associated Analytic Story

• Cobalt Strike

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• EventID

• process_name

• process_id

• parent_process_name

• dest_port

• process_path

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives may be present in small environments. Tuning may be required based on parent process.

Reference

• https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile

• https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log

version: 1

---

Hide User Account From Sign-In Screen

This analytic identifies a suspicious registry modification to hide a user account on the Windows Login screen. This technique was seen in some tradecraft where the adversary will create a hidden user account with Admin privileges in login screen to avoid noticing by the user that they already compromise and to persist on that said machine.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-05-05

details

Search

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_name = "DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_value_name 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `hide_user_account_from_sign_in_screen_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as CarbonBlack or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.registry_value_name

• Registry.dest Registry.user

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Unknown. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log

version: 1

---

Hiding Files And Directories With Attrib exe

Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1222.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)`
| `hiding_files_and_directories_with_attrib_exe_filter` 

Associated Analytic Story

• Windows Defense Evasion Tactics

• Windows Persistence Techniques

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.parent_process

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1222.001	Windows File and Directory Permissions Modification	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Some applications and users may legitimately use attrib.exe to interact with the files.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log

version: 4

---

High File Deletion Frequency

This search looks for high frequency of file deletion relative to process name and process id. These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the original files to be deleted as soon it was replace as encrypted data.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1485
• Last Updated: 2021-03-16

details

Search

`sysmon` EventCode=23 TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") 
| stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID 
|where count >=100 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `high_file_deletion_frequency_filter`

Associated Analytic Story

• Clop Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• EventCode

• TargetFilename

• Computer

• user

• Image

• ProcessID

• _time

ATT&CK

ID	Technique	Tactic
T1485	Data Destruction	Impact

Kill Chain Phase

• Exploitation

Known False Positives

user may delete bunch of pictures or files in a folder.

Reference

• https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

• https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log

version: 1

---

High Number of Login Failures from a single source

This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1110.001
• Last Updated: 2020-12-16

details

Search

`o365_management_activity` Operation=UserLoginFailed  record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory 
| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app 
| search accounts_locked >= 5
| `high_number_of_login_failures_from_a_single_source_filter`

Associated Analytic Story

• Office 365 Detections

How To Implement

Required field

• _time

• Operation

• record_type

• app

• user

• LogonError

• authentication_method

• signature

• UserAgent

• src_ip

• record_type

ATT&CK

ID	Technique	Tactic
T1110.001	Password Guessing	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

unknown

Reference

Test Dataset

version: 1

---

High Process Termination Frequency

This analytics are designed to indentify a high frequency of process termination on a machine which is a common behavior of ransomware malware before encrypting files. This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1486
• Last Updated: 2021-03-16

details

Search

`sysmon` EventCode=5 
|bin _time span=3s 
|stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime  count by Computer EventCode ProcessID 
| where count >= 15 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `high_process_termination_frequency_filter`

Associated Analytic Story

• Clop Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• EventCode

• Image

• Computer

• _time

• ProcessID

ATT&CK

ID	Technique	Tactic
T1486	Data Encrypted for Impact	Impact

Kill Chain Phase

• Exploitation

Known False Positives

admin or user tool that can terminate multiple process.

Reference

• https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

• https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log

version: 1

---

Hosts receiving high volume of network traffic from email server

This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1114.002
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d 
| `drop_dm_object_name("All_Traffic")` 
| eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in 
| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip 
| eval minimum_data_samples = 4, deviation_threshold = 3 
| where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") 
| eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) 
| table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average 
| `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`

Associated Analytic Story

• Collection and Staging

How To Implement

This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.

Required field

• _time

• All_Traffic.bytes_in

• All_Traffic.dest_category

• All_Traffic.src_ip

ATT&CK

ID	Technique	Tactic
T1114.002	Remote Email Collection	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.

Reference

Test Dataset

version: 2

---

ICACLS Grant Command

This analytic identifies potential adversaries that modify the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft and coinminer scripts to evade detections and restrict access to their component files.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1222
• Last Updated: 2021-05-04

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/grant*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `icacls_grant_command_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed icacls.exe may be used.

Required field

• _time

• Processes.parent_process_name

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.process_id

• Processes.process

ATT&CK

ID	Technique	Tactic
T1222	File and Directory Permissions Modification	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Unknown. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Icacls Deny Command

This analytic identifies a potential adversary that changes the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft or coinminer scripts. This behavior is meant to evade detection and prevent access to their component files.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1222
• Last Updated: 2021-04-29

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/deny*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `icacls_deny_command_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed icacls.exe may be used.

Required field

• _time

• Processes.parent_process_name

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.process_id

• Processes.process

ATT&CK

ID	Technique	Tactic
T1222	File and Directory Permissions Modification	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Unknown. It is possible some administrative scripts use ICacls. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Identify New User Accounts

This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.002
• Last Updated: 2017-09-12

details

Search

| from datamodel Identity_Management.All_Identities  
| eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") 
| search empStatus="Accounts created in last week"
| `security_content_ctime(endDate)` 
| `security_content_ctime(startDate)`
| table identity empStatus endDate startDate 
| `identify_new_user_accounts_filter`

Associated Analytic Story

• Account Monitoring and Controls

How To Implement

To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1078.002	Domain Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately.

Reference

Test Dataset

version: 1

---

Illegal Access To User Content via PowerSploit modules

This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1021, T1113, T1123, T1563
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Malicious PowerShell

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1021	Remote Services	Lateral Movement
T1113	Screen Capture	Collection
T1123	Audio Capture	Collection
T1563	Remote Service Session Hijacking	Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/illegal_access_to_content/logAllPowerSploitModulesWithOldNames.log

version: 1

---

Illegal Account Creation via PowerSploit modules

This detection identifies access to PowerSploit modules that create accounts illegaly.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1585
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1585	Establish Accounts	Resource Development

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1585/illegal_account_creation/logAllPowerSploitModulesWithOldNames.log

version: 1

---

Illegal Deletion of Logs via Mimikatz modules

This detection identifies access to PowerSploit modules that delete event logs.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1070
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Log Manipulation

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1070	Indicator Removal on Host	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/illegal_log_deletion/logAllMimikatzModules.log

version: 1

---

Illegal Enabling or Disabling of Accounts via DSInternals modules

This detection identifies use of DSInternals modules that enable or disable accounts illegaly.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1078, T1098
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/MichaelGrafnetter/DSInternals

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllDSInternalsModules.log

version: 1

---

Illegal Management of Active Directory Elements and Policies via DSInternals modules

This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1098, T1207, T1484
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1098	Account Manipulation	Persistence
T1207	Rogue Domain Controller	Defense Evasion
T1484	Domain Policy Modification	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/MichaelGrafnetter/DSInternals

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/logAllDSInternalsModules.log

version: 1

---

Illegal Management of Computers and Active Directory Elements via PowerSploit modules

This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1098, T1207, T1484
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true )


| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1098	Account Manipulation	Persistence
T1207	Rogue Domain Controller	Defense Evasion
T1484	Domain Policy Modification	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/logAllPowerSploitModulesWithOldNames.log

version: 1

---

Illegal Privilege Elevation and Persistence via PowerSploit modules

This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1053, T1134, T1548
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Malicious PowerShell

• Windows Persistence Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1053	Scheduled Task/Job	Execution, Persistence, Privilege Escalation
T1134	Access Token Manipulation	Defense Evasion, Privilege Escalation
T1548	Abuse Elevation Control Mechanism	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/logAllPowerSploitModulesWithOldNames.log

version: 1

---

Illegal Privilege Elevation via Mimikatz modules

This detection identifies use of Mimikatz modules for illegal privilege elevation.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1134, T1548
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Privilege Escalation

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1134	Access Token Manipulation	Defense Evasion, Privilege Escalation
T1548	Abuse Elevation Control Mechanism	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/logAllMimikatzModules.log

version: 1

---

Illegal Service and Process Control via Mimikatz modules

This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1055, T1106, T1569
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Service Abuse

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation
T1106	Native API	Execution
T1569	System Services	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllMimikatzModules.log

version: 1

---

Illegal Service and Process Control via PowerSploit modules

This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1055, T1106, T1569
• Last Updated: 2020-11-09

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true )


| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Service Abuse

• Malicious PowerShell

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation
T1106	Native API	Execution
T1569	System Services	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllPowerSploitModulesWithOldNames.log

version: 1

---

Kerberoasting spn request with RC4 encryption

This search detects a potential kerberoasting attack via service principal name requests

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1558.003
• Last Updated: 2020-10-16

details

Search

`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `kerberoasting_spn_request_with_rc4_encryption_filter`

Associated Analytic Story

• Lateral Movement

How To Implement

You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos

Required field

• _time

• EventCode

• Ticket_Options

• Ticket_Encryption_Type

• dest

• service

• service_id

ATT&CK

ID	Technique	Tactic
T1558.003	Kerberoasting	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Older systems that support kerberos RC4 by default NetApp may generate false positives

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md

• https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-security.log

version: 3

---

Known Services Killed by Ransomware

This search detects a suspicioous termination of known services killed by ransomware before encrypting files in a compromised machine. This technique is commonly seen in most of ransomware now a days to avoid exception error while accessing the targetted files it wants to encrypts because of the open handle of those services to the targetted file.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1490
• Last Updated: 2021-06-04

details

Search

`wineventlog_system` EventCode=7036 Message IN ("*Volume Shadow Copy*","*VSS*", "*backup*", "*sophos*", "*sql*", "*memtas*", "*mepocs*", "*veeam*", "*svc$*") Message="*service entered the stopped state*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message dest Type 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `known_services_killed_by_ransomware_filter`

Associated Analytic Story

• Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the 7036 EventCode ScManager in System audit Logs from your endpoints.

Required field

• _time

• EventCode

• Message

• dest

• Type

ATT&CK

ID	Technique	Tactic
T1490	Inhibit System Recovery	Impact

Kill Chain Phase

• Exploitation

Known False Positives

Admin activities or installing related updates may do a sudden stop to list of services we monitor.

Reference

• https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

• https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf3/windows-system.log

version: 1

---

Kubernetes AWS detect RBAC authorization by account

This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-06-23

details

Search

`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* 
| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason 
| stats count by user.username annotations.authorization.k8s.io/reason 
| rare user.username annotations.authorization.k8s.io/reason 
|`kubernetes_aws_detect_rbac_authorization_by_account_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.

Reference

Test Dataset

version: 1

---

Kubernetes AWS detect most active service accounts by pod

This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-06-23

details

Search

`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts  objectRef.resource=pods 
| table  sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision  
| top  sourceIPs{} user.username verb annotations.authorization.k8s.io/decision 
|`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness.

Reference

Test Dataset

version: 1

---

Kubernetes AWS detect sensitive role access

This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-06-23

details

Search

`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1  
| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason 
| dedup user.username user.groups{} 
|`kubernetes_aws_detect_sensitive_role_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use.

Reference

Test Dataset

version: 1

---

Kubernetes AWS detect service accounts forbidden failure access

This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-06-23

details

Search

`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure 
| table sourceIPs{} user.username userAgent verb responseStatus.status requestURI 
| `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

This search can give false positives as there might be inherent issues with authentications and permissions at cluster.

Reference

Test Dataset

version: 1

---

Kubernetes AWS detect suspicious kubectl calls

This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-06-23

details

Search

`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous  
| table  src_ip src_user verb userAgent requestURI  
| stats  count by src_ip src_user verb userAgent requestURI 
|`kubernetes_aws_detect_suspicious_kubectl_calls_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.

Required field

• _time

• userAgent

• sourceIPs{}

• src_user

• src_ip

• verb

• requestURI

Kill Chain Phase

• Lateral Movement

Known False Positives

Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets

Reference

Test Dataset

version: 1

---

Kubernetes Azure detect RBAC authorization by account

This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-05-26

details

Search

`kubernetes_azure` category=kube-audit 
| spath input=properties.log 
| search annotations.authorization.k8s.io/reason=* 
| table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason 
|stats count by user.username annotations.authorization.k8s.io/reason 
| rare user.username annotations.authorization.k8s.io/reason 
|`kubernetes_azure_detect_rbac_authorization_by_account_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.

Reference

Test Dataset

version: 1

---

Kubernetes Azure detect most active service accounts by pod namespace

This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-05-26

details

Search

`kubernetes_azure` category=kube-audit 
| spath input=properties.log 
| search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow  
| table  sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace 
| top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace 
|`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness.

Reference

Test Dataset

version: 1

---

Kubernetes Azure detect sensitive object access

This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-05-20

details

Search

`kubernetes_azure` category=kube-audit 
| spath input=properties.log
| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow  
|table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason 
|dedup user.username user.groups{} 
|`kubernetes_azure_detect_sensitive_object_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.

Reference

Test Dataset

version: 1

---

Kubernetes Azure detect sensitive role access

This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-05-20

details

Search

`kubernetes_azure` category=kube-audit 
| spath input=properties.log
| search objectRef.resource=clusterroles OR clusterrolebindings 
| table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason 
| dedup user.username user.groups{} 
|`kubernetes_azure_detect_sensitive_role_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use.

Reference

Test Dataset

version: 1

---

Kubernetes Azure detect service accounts forbidden failure access

This search provides information on Kubernetes service accounts with failure or forbidden access status

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-05-20

details

Search

`kubernetes_azure` category=kube-audit 
| spath input=properties.log 
| search user.groups{}=system:serviceaccounts*  responseStatus.reason=Forbidden 
| table  sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace  
|`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

This search can give false positives as there might be inherent issues with authentications and permissions at cluster.

Reference

Test Dataset

version: 1

---

Kubernetes Azure detect suspicious kubectl calls

This search provides information on rare Kubectl calls with IP, verb namespace and object access context

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-05-26

details

Search

`kubernetes_azure` category=kube-audit 
| spath input=properties.log 
| spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration 
| search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 
| table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI 
| rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI 
|`kubernetes_azure_detect_suspicious_kubectl_calls_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets

Reference

Test Dataset

version: 1

---

Kubernetes Azure pod scan fingerprint

This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-05-20

details

Search

`kubernetes_azure` category=kube-audit 
| spath input=properties.log 
| search responseStatus.code=401 
| table  sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod 
|`kubernetes_azure_pod_scan_fingerprint_filter`

Associated Analytic Story

• Kubernetes Scanning Activity

How To Implement

You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics

Required field

• _time

Kill Chain Phase

• Reconnaissance

Known False Positives

Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context.

Reference

Test Dataset

version: 1

---

Kubernetes Azure scan fingerprint

This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1526
• Last Updated: 2020-05-19

details

Search

`kubernetes_azure` category=kube-audit 
| spath input=properties.log 
| search responseStatus.code=401 
| table  sourceIPs{} userAgent verb requestURI responseStatus.reason 
|`kubernetes_azure_scan_fingerprint_filter`

Associated Analytic Story

• Kubernetes Scanning Activity

How To Implement

You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1526	Cloud Service Discovery	Discovery

Kill Chain Phase

• Reconnaissance

Known False Positives

Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context.

Reference

Test Dataset

version: 1

---

Kubernetes GCP detect RBAC authorizations by account

This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-07-11

details

Search

`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole  
| table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason 
| rare src_user data.labels.authorization.k8s.io/reason 
|`kubernetes_gcp_detect_rbac_authorizations_by_account_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.

Reference

Test Dataset

version: 1

---

Kubernetes GCP detect most active service accounts by pod

This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-07-10

details

Search

`google_gcp_pubsub_message`  data.protoPayload.request.spec.group{}=system:serviceaccounts 
| table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource 
| top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource 
|`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install splunk GCP add on. This search works with pubsub messaging service logs

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness.

Reference

Test Dataset

version: 1

---

Kubernetes GCP detect sensitive object access

This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-07-11

details

Search

`google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets  
| table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision 
| dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name 
|`kubernetes_gcp_detect_sensitive_object_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install splunk add on for GCP . This search works with pubsub messaging service logs.

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.

Reference

Test Dataset

version: 1

---

Kubernetes GCP detect sensitive role access

This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-07-11

details

Search

`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1  
| table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason 
| dedup src_ip src_user 
|`kubernetes_gcp_detect_sensitive_role_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Role Activity

How To Implement

You must install splunk add on for GCP. This search works with pubsub messaging servicelogs.

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use.

Reference

Test Dataset

version: 1

---

Kubernetes GCP detect service accounts forbidden failure access

This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-06-23

details

Search

`google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* 
| table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb  data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision 
| dedup src_ip src_user 
| `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install splunk add on for GCP. This search works with pubsub messaging service logs.

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

This search can give false positives as there might be inherent issues with authentications and permissions at cluster.

Reference

Test Dataset

version: 1

---

Kubernetes GCP detect suspicious kubectl calls

This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-07-11

details

Search

`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous 
| table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path 
|dedup src_ip src_user 
|`kubernetes_gcp_detect_suspicious_kubectl_calls_filter`

Associated Analytic Story

• Kubernetes Sensitive Object Access Activity

How To Implement

You must install splunk add on for GCP. This search works with pubsub messaging logs.

Required field

• _time

Kill Chain Phase

• Lateral Movement

Known False Positives

Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets

Reference

Test Dataset

version: 1

---

Large Volume of DNS ANY Queries

The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK: T1498.002
• Last Updated: 2017-09-20

details

Search

| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" 
| `drop_dm_object_name("DNS")` 
| where count>200 
| `large_volume_of_dns_any_queries_filter`

Associated Analytic Story

• DNS Amplification Attacks

How To Implement

To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.

Required field

• _time

• DNS.message_type

• DNS.record_type

• DNS.dest

ATT&CK

ID	Technique	Tactic
T1498.002	Reflection Amplification	Impact

Kill Chain Phase

• Actions on Objectives

Known False Positives

Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment.

Reference

Test Dataset

version: 1

---

MacOS - Re-opened Applications

This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK:
• Last Updated: 2020-02-07

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `macos___re_opened_applications_filter`

Associated Analytic Story

How To Implement

In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the splunk.conf pack enabled. Also the TA-OSquery must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.user

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

Kill Chain Phase

• Installation

• Command and Control

Known False Positives

At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list.

Reference

Test Dataset

version: 1

---

Mailsniper Invoke functions

This search is to detect known mailsniper.ps1 functions executed in a machine. This technique was seen in some attacker to harvest some sensitive e-mail in a compromised exchange server.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1114.001
• Last Updated: 2021-05-19

details

Search

`powershell` EventCode=4104 Message IN ("*Invoke-GlobalO365MailSearch*", "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*", "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*", "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `mailsniper_invoke_functions_filter`

Associated Analytic Story

• Data Exfiltration

How To Implement

To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event.

Required field

• _time

• EventCode

• Message

• ComputerName

• User

ATT&CK

ID	Technique	Tactic
T1114.001	Local Email Collection	Collection

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://www.blackhillsinfosec.com/introducing-mailsniper-a-tool-for-searching-every-users-email-for-sensitive-data/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-powershell.log

version: 1

---

Malicious PowerShell Process - Connect To Internet With Hidden Window

This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001
• Last Updated: 2020-11-20

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `malicious_powershell_process___connect_to_internet_with_hidden_window_filter`

Associated Analytic Story

• Malicious PowerShell

• Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

• HAFNIUM Group

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.user

• Processes.parent_process_name

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

Legitimate process can have this combination of command-line options, but it's not common.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log

version: 5

---

Malicious PowerShell Process - Encoded Command

This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1027
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `malicious_powershell_process___encoded_command_filter`

Associated Analytic Story

• Malicious PowerShell

• NOBELIUM Group

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.user

• Processes.parent_process_name

• Processes.dest

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1027	Obfuscated Files or Information	Defense Evasion

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

System administrators may use this option, but it's not common.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log

version: 4

---

Malicious PowerShell Process - Execution Policy Bypass

This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `malicious_powershell_process___execution_policy_bypass_filter`

Associated Analytic Story

• DHS Report TA18-074A

• HAFNIUM Group

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_id

• Processes.parent_process_id

• Processes.process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log

version: 4

---

Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments

This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001
• Last Updated: 2021-01-19

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* 
| `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter`

Associated Analytic Story

• Malicious PowerShell

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

Legitimate process can have this combination of command-line options, but it's not common.

Reference

Test Dataset

version: 6

---

Malicious PowerShell Process With Obfuscation Techniques

This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001
• Last Updated: 2021-01-19

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) 
| `malicious_powershell_process_with_obfuscation_techniques_filter` 
| search num_obfuscation > 10 

Associated Analytic Story

• Malicious PowerShell

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.parent_process_name

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution

Kill Chain Phase

• Command and Control

• Actions on Objectives

Known False Positives

These characters might be legitimately on the command-line, but it is not common.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log

version: 4

---

Malicious Powershell Executed As A Service

This detection is to identify the abuse the Windows SC.exe to execute malicious commands or payloads via PowerShell.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1569.002
• Last Updated: 2021-04-07

details

Search

 `wineventlog_system` EventCode=7045 
| eval l_Service_File_Name=lower(Service_File_Name) 
| regex l_Service_File_Name="powershell[.\s]
|powershell_ise[.\s]
|pwsh[.\s]
|psexec[.\s]" 
| regex l_Service_File_Name="-nop[rofile]*
|-w[indowstyle]*\s+hid[den]*
|-noe[xit]*
|-enc[odedcommand]*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type Service_Account user 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `malicious_powershell_executed_as_a_service_filter`

Associated Analytic Story

• Malicious Powershell

How To Implement

To successfully implement this search, you need to be ingesting Windows System logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.

Required field

• EventCode

• Service_File_Name

• Service_Type

• _time

• Service_Name

• Service_Start_Type

• Service_Account

• user

ATT&CK

ID	Technique	Tactic
T1569.002	Service Execution	Execution

Kill Chain Phase

• Privilege Escalation

Known False Positives

Creating a hidden powershell service is rare and could key off of those instances.

Reference

• https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/dosfuscation-report.pdf

• http://az4n6.blogspot.com/2017/

• https://www.danielbohannon.com/blog-1/2017/3/12/powershell-execution-argument-obfuscation-how-it-can-make-detection-easier

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-system.log

version: 1

---

Modification Of Wallpaper

This analytic identifies suspicious modification of registry to deface or change the wallpaper of a compromised machines as part of its payload. This technique was commonly seen in ransomware like REVIL where it create a bitmap file contain a note that the machine was compromised and make it as a wallpaper.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1491
• Last Updated: 2021-06-02

details

Search

`sysmon` EventCode =13  (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Image != "*\\explorer.exe") OR (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Details = "*\\temp\\*") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Image TargetObject Details  Computer process_guid process_id user_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `modification_of_wallpaper_filter`

Associated Analytic Story

• Ransomware

• Revil Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the Image, TargetObject registry key, registry Details from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventCode

• Image

• TargetObject

• Details

• Computer

• process_guid

• process_id

• user_id

ATT&CK

ID	Technique	Tactic
T1491	Defacement	Impact

Kill Chain Phase

• Exploitation

Known False Positives

3rd party tool may used to changed the wallpaper of the machine

Reference

• https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

• https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log

version: 1

---

Modify ACL permission To Files Or Folder

This analytic identifies suspicious modification of ACL permission to a files or folder to make it available to everyone. This technique may be used by the adversary to evade ACLs or protected files access. This changes is commonly configured by the file or directory owner with appropriate permission. This behavior is a good indicator if this command seen on a machine utilized by an account with no permission to do so.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1222
• Last Updated: 2021-05-04

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "xcacls.exe" AND (Processes.process = "*/G everyone:*"  OR Processes.process = "*/G SYSTEM:*") by Processes.parent_process_name Processes.process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `modify_acl_permission_to_files_or_folder_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed cacls.exe may be used.

Required field

• _time

• Processes.parent_process_name

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.process

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1222	File and Directory Permissions Modification	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

administrators may use this command. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Monitor DNS For Brand Abuse

This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Resolution
• ATT&CK:
• Last Updated: 2017-09-23

details

Search

| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query 
| `drop_dm_object_name("DNS")` 
| `security_content_ctime(firstTime)`
| `brand_abuse_dns` 
| `monitor_dns_for_brand_abuse_filter`

Associated Analytic Story

• Brand Monitoring

How To Implement

You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.

Required field

• _time

Kill Chain Phase

• Delivery

• Actions on Objectives

Known False Positives

None at this time

Reference

Test Dataset

version: 1

---

Monitor Email For Brand Abuse

This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Email
• ATT&CK:
• Last Updated: 2018-01-05

details

Search

| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id 
| `drop_dm_object_name("All_Email")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| eval temp=split(src_user, "@") 
| eval email_domain=mvindex(temp, 1) 
| lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse 
| search domain_abuse=true 
| table message_id, src_user, email_domain, recipients, firstTime, lastTime 
| `monitor_email_for_brand_abuse_filter`

Associated Analytic Story

• Brand Monitoring

• Suspicious Emails

How To Implement

You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.

Required field

• _time

• All_Email.recipient

• All_Email.src_user

• All_Email.message_id

Kill Chain Phase

• Delivery

Known False Positives

None at this time

Reference

Test Dataset

version: 2

---

Monitor Registry Keys for Print Monitors

This search looks for registry activity associated with modifications to the registry key HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1547.010
• Last Updated: 2020-11-23

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action 
| `drop_dm_object_name(Registry)` 
| `monitor_registry_keys_for_print_monitors_filter`

Associated Analytic Story

• Suspicious Windows Registry Activities

• Windows Persistence Techniques

How To Implement

To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.

Required field

• _time

• Registry.action

• Registry.registry_path

• Registry.dest

• Registry.registry_key_name

• Registry.user

• Registry.registry_value_name

ATT&CK

ID	Technique	Tactic
T1547.010	Port Monitors	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

You will encounter noise from legitimate print-monitor registry entries.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/windows-sysmon.log

version: 2

---

Monitor Web Traffic For Brand Abuse

This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Web
• ATT&CK:
• Last Updated: 2017-09-23

details

Search

| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `brand_abuse_web` 
| `monitor_web_traffic_for_brand_abuse_filter`

Associated Analytic Story

• Brand Monitoring

How To Implement

You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.

Required field

• _time

• Web.url

• Web.src

Kill Chain Phase

• Delivery

Known False Positives

None at this time

Reference

Test Dataset

version: 1

---

More than usual number of LOLBAS applications in short time period

Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1059, T1053
• Last Updated: 2020-08-25

details

Search

 
| from read_ssa_enriched_events() 
| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" 
| stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) 
| eval lolbas_counter=lolbas_counter*1.0 
| rename window_end as timestamp 
| adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L 
| where label AND quantile>0.99 
| eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body=create_map(["process_name", process_name]) 
| into write_null();

Associated Analytic Story

• Unusual Processes

How To Implement

Collect endpoint data such as sysmon or 4688 events.

Required field

• dest_device_id

• _time

• process_name

ATT&CK

ID	Technique	Tactic
T1059	Command and Scripting Interpreter	Execution
T1053	Scheduled Task/Job	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Some administrative tasks may involve multiple use of LOLBAS applications in a short period of time. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline.

Reference

• https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries

Test Dataset

version: 1

---

Multiple Archive Files Http Post Traffic

This search is designed to detect high frequency of archive files data exfiltration through HTTP POST method protocol. This are one of the common techniques used by APT or trojan spy after doing the data collection like screenshot, recording, sensitive data to the infected machines. The attacker may execute archiving command to the collected data, save it a temp folder with a hidden attribute then send it to its C2 through HTTP POST. Sometimes adversaries will rename the archive files or encode/encrypt to cover their tracks. This detection can detect a renamed archive files transfer to HTTP POST since it checks the request body header. Unfortunately this detection cannot support archive that was encrypted or encoded before doing the exfiltration.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1048.003
• Last Updated: 2021-04-21

details

Search

`stream_http` http_method=POST 
|eval archive_hdr1=substr(form_data,1,2) 
| eval archive_hdr2 = substr(form_data,1,4) 
|stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out archive_hdr1 archive_hdr2 
|where count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!") 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `multiple_archive_files_http_post_traffic_filter`

Associated Analytic Story

• Command and Control

How To Implement

To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled in stream http configuration.

Required field

• _time

• http_method

• http_user_agent

• uri_path

• url

• bytes_in

• bytes_out

• archive_hdr1

• archive_hdr2

ATT&CK

ID	Technique	Tactic
T1048.003	Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol	Exfiltration

Kill Chain Phase

• Exfiltration

Known False Positives

Normal archive transfer via HTTP protocol may trip this detection.

Reference

• https://attack.mitre.org/techniques/T1560/001/

• https://www.fireeye.com/blog/threat-research/2019/01/apt39-iranian-cyber-espionage-group-focused-on-personal-information.html

• https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/archive_http_post/stream_http_events.log

version: 1

---

Multiple Disabled Users Failing To Authenticate From Host Using Kerberos

The following analytic identifies one source endpoint failing to authenticate with multiple disabled domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack against disabled users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code 0x12 stands for clients credentials have been revoked (account disabled, expired or locked out).
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.
This detection will only trigger on domain controllers, not on member servers or workstations.
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1110.003
• Last Updated: 2021-04-14

details

Search

`wineventlog_security` EventCode=4768 Account_Name!="*$" Result_Code=0x12 
| bucket span=2m _time 
| stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_disabled_users_failing_to_authenticate_from_host_using_kerberos_filter` 

Associated Analytic Story

• Active Directory Password Spraying

How To Implement

To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting Audit Kerberos Authentication Service within Account Logon needs to be enabled.

Required field

• _time

• EventCode

• Result_Code

• Account_Name

• Client_Address

ATT&CK

ID	Technique	Tactic
T1110.003	Password Spraying	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

A host failing to authenticate with multiple disabled domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems missconfigured systems.

Reference

• https://attack.mitre.org/techniques/T1110/003/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos/windows-security.log

version: 1

---

Multiple Invalid Users Failing To Authenticate From Host Using Kerberos

The following analytic identifies one source endpoint failing to authenticate with multiple invalid domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code 0x6 stands for client not found in Kerberos database (the attempted user is not a valid domain user).
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.
This detection will only trigger on domain controllers, not on member servers or workstations.
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1110.003
• Last Updated: 2021-04-14

details

Search

`wineventlog_security` EventCode=4768 Result_Code=0x6 Account_Name!="*$" 
| bucket span=2m _time 
| stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_invalid_users_failing_to_authenticate_from_host_using_kerberos_filter` 

Associated Analytic Story

• Active Directory Password Spraying

How To Implement

To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting Audit Kerberos Authentication Service within Account Logon needs to be enabled.

Required field

• _time

• EventCode

• Result_Code

• Account_Name

• Client_Address

ATT&CK

ID	Technique	Tactic
T1110.003	Password Spraying	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems and missconfigured systems.

Reference

• https://attack.mitre.org/techniques/T1110/003/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos/windows-security.log

version: 1

---

Multiple Invalid Users Failing To Authenticate From Host Using NTLM

The following analytic identifies one source endpoint failing to authenticate with multiple invalid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC0000064 stands for The username you typed does not exist (the attempted user is a legitimate domain user).
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.
This detection will only trigger on domain controllers, not on member servers or workstations.
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1110.003
• Last Updated: 2021-04-15

details

Search

 `wineventlog_security` EventCode=4776 Logon_Account!="*$" 0xC0000064 action=failure 
| bucket span=2m _time 
| stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_invalid_users_failing_to_authenticate_from_host_using_ntlm_filter`

Associated Analytic Story

• Active Directory Password Spraying

How To Implement

To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting Audit Credential Validation' within Account Logon` needs to be enabled.

Required field

• _time

• EventCode

• action

• Logon_Account

• Source_Workstation

ATT&CK

ID	Technique	Tactic
T1110.003	Password Spraying	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.

Reference

• https://attack.mitre.org/techniques/T1110/003/

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm/windows-security.log

version: 1

---

Multiple Okta Users With Invalid Credentials From The Same IP

This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.001
• Last Updated: 2020-07-21

details

Search

`okta` outcome.reason=INVALID_CREDENTIALS 
| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city 
| stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city  
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
|  search distinct_users > 5
| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` 

Associated Analytic Story

• Suspicious Okta Activity

How To Implement

This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

Required field

• _time

• outcome.reason

• client.geographicalContext.country

• client.geographicalContext.state

• client.geographicalContext.city

• user

• src_ip

• displayMessage

ATT&CK

ID	Technique	Tactic
T1078.001	Default Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter to raise the threshold or except specific IP adresses from triggering this search.

Reference

Test Dataset

version: 2

---

Multiple Users Attempting To Authenticate Using Explicit Credentials

The following analytic identifies a source user failing to authenticate with multiple users using explicit credentials on a host. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4648 is generated when a process attempts an account logon by explicitly specifying that accounts credentials. This event generates on domain controllers, member servers, and workstations.
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.
This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed.
The analytics returned fields allow analysts to investigate the event further by providing fields like source account, attempted user accounts and the endpoint were the behavior was identified.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1110.003
• Last Updated: 2021-04-13

details

Search

 `wineventlog_security` EventCode=4648 
| bucket span=2m _time 
| eval Source_Account = mvindex(Account_Name, 0) 
| eval Destination_Account = mvindex(Account_Name, 1) 
| search Source_Account != "*$" Source_Account !="-" Destination_Account !="*$" 
| stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_account by _time, ComputerName, Source_Account 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by ComputerName 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_attempting_to_authenticate_using_explicit_credentials_filter` 

Associated Analytic Story

• Active Directory Password Spraying

How To Implement

To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting Audit Logon within Logon/Logoff needs to be enabled.

Required field

• _time

• EventCode

• Security_ID

• Account_Name

• ComputerName

ATT&CK

ID	Technique	Tactic
T1110.003	Password Spraying	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

A source user failing attempting to authenticate multiple users on a host is not a common behavior for regular systems. Some applications, however, may exhibit this behavior in which case sets of users hosts can be added to an allow list. Possible false positive scenarios include systems where several users connect to like Mail servers, identity providers, remote desktop services, Citrix, etc.

Reference

• https://attack.mitre.org/techniques/T1110/003/

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4648

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray/windows-security.log

version: 1

---

Multiple Users Failing To Authenticate From Host Using Kerberos

The following analytic identifies one source endpoint failing to authenticate with multiple valid users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Event 4771 is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT). Failure code 0x18 stands for wrong password provided (the attempted user is a legitimate domain user).
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.
This detection will only trigger on domain controllers, not on member servers or workstations.
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1110.003
• Last Updated: 2021-04-08

details

Search

`wineventlog_security` EventCode=4771 Failure_Code=0x18 Account_Name!="*$" 
| bucket span=2m _time 
| stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_failing_to_authenticate_from_host_using_kerberos_filter`

Associated Analytic Story

• Active Directory Password Spraying

How To Implement

To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting Audit Kerberos Authentication Service within Account Logon needs to be enabled.

Required field

• _time

• EventCode

• Result_Code

• Account_Name

• Client_Address

ATT&CK

ID	Technique	Tactic
T1110.003	Password Spraying	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, missconfigured systems and multi-user systems like Citrix farms.

Reference

• https://attack.mitre.org/techniques/T1110/003/

• https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319109(v=ws.11)

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4771

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos/windows-security.log

version: 1

---

Multiple Users Failing To Authenticate From Host Using NTLM

The following analytic identifies one source endpoint failing to authenticate with multiple valid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC000006A means: misspelled or bad password (the attempted user is a legitimate domain user).
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.
This detection will only trigger on domain controllers, not on member servers or workstations.
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1110.003
• Last Updated: 2021-04-13

details

Search

 `wineventlog_security` EventCode=4776 Logon_Account!="*$" 0xC000006A action=failure 
| bucket span=2m _time 
| stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_failing_to_authenticate_from_host_using_ntlm_filter`

Associated Analytic Story

• Active Directory Password Spraying

How To Implement

To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting Audit Credential Validation within Account Logon needs to be enabled.

Required field

• _time

• EventCode

• action

• Logon_Account

• Source_Workstation

ATT&CK

ID	Technique	Tactic
T1110.003	Password Spraying	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.

Reference

• https://attack.mitre.org/techniques/T1110/003/

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm/windows-security.log

version: 1

---

Multiple Users Failing To Authenticate From Process

The following analytic identifies a source process name failing to authenticate with multiple users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4625 generates on domain controllers, member servers, and workstations when an account fails to logon. Logon Type 2 describes an iteractive logon attempt.
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.
This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed. This could be a domain controller as well as a member server or workstation.
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1110.003
• Last Updated: 2021-04-13

details

Search

 `wineventlog_security` EventCode=4625 Logon_Type=2 Caller_Process_Name!="-" 
| bucket span=2m _time 
| eval Source_Account = mvindex(Account_Name, 0) 
| eval Destination_Account = mvindex(Account_Name, 1) 
| stats dc(Destination_Account) AS unique_accounts values(Account_Name) as tried_accounts by _time, Caller_Process_Name, Source_Account, ComputerName 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Caller_Process_Name, Source_Account, ComputerName 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_failing_to_authenticate_from_process_filter` 

Associated Analytic Story

• Active Directory Password Spraying

How To Implement

To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers aas well as member servers and workstations. The Advanced Security Audit policy setting Audit Logon within Logon/Logoff needs to be enabled.

Required field

• _time

• EventCode

• Logon_Type

• Caller_Process_Name

• Security_ID

• Account_Name

• ComputerName

ATT&CK

ID	Technique	Tactic
T1110.003	Password Spraying	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

A process failing to authenticate with multiple users is not a common behavior for legitimate user sessions. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.

Reference

• https://attack.mitre.org/techniques/T1110/003/

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625

• https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process/windows-security.log

version: 1

---

Multiple Users Remotely Failing To Authenticate From Host

The following analytic identifies a source host failing to authenticate against a remote host with multiple users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4625 documents each and every failed attempt to logon to the local computer. This event generates on domain controllers, member servers, and workstations. Logon Type 3 describes an remote authentication attempt.
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.
This detection will trigger on the host that is the target of the password spraying attack. This could be a domain controller as well as a member server or workstation.
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1110.003
• Last Updated: 2021-04-13

details

Search

 `wineventlog_security` EventCode=4625 Logon_Type=3 Source_Network_Address!="-" 
| bucket span=2m _time 
| eval Destination_Account = mvindex(Account_Name, 1) 
| stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_accounts by _time, Source_Network_Address, ComputerName 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Network_Address, ComputerName 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_remotely_failing_to_authenticate_from_host_filter` 

Associated Analytic Story

• Active Directory Password Spraying

How To Implement

To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as as well as member servers and workstations. The Advanced Security Audit policy setting Audit Logon within Logon/Logoff needs to be enabled.

Required field

• _time

• EventCode

• Logon_Type

• Security_ID

• Account_Name

• ComputerName

• Source_Network_Address

ATT&CK

ID	Technique	Tactic
T1110.003	Password Spraying	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

A host failing to authenticate with multiple valid users against a remote host is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, missconfigyred systems, etc.

Reference

• https://attack.mitre.org/techniques/T1110/003/

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625

• https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625

• https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray/windows-security.log

version: 1

---

NLTest Domain Trust Discovery

This search looks for the execution of nltest.exe with command-line arguments utilized to query for Domain Trust information. Two arguments /domain trusts, returns a list of trusted domains, and /all_trusts, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1482
• Last Updated: 2021-01-25

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `nltest_domain_trust_discovery_filter`

Associated Analytic Story

• Ryuk Ransomware

• Domain Trust Discovery

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1482	Domain Trust Discovery	Discovery

Kill Chain Phase

• Exploitation

Known False Positives

Administrators may use nltest for troubleshooting purposes, otherwise, rarely used.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md

• https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104

• https://attack.mitre.org/techniques/T1482/

• https://www.owasp.org/images/4/4b/Red_Team_Operating_in_a_Modern_Environment.pdf

• https://ss64.com/nt/nltest.html

• https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/

• https://thedfirreport.com/2020/10/08/ryuks-return/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log

version: 1

---

New container uploaded to AWS ECR

This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1525
• Last Updated: 2020-02-20

details

Search

| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type 
| `drop_dm_object_name("Compute")` 
| `new_container_uploaded_to_aws_ecr_filter` 

Associated Analytic Story

• Container Implantation Monitoring and Investigation

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the container_implant_aws_detection_filter macro to filter out the false positives.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1525	Implant Internal Image	Persistence

Kill Chain Phase

Known False Positives

Uploading container is a normal behavior from developers or users with access to container registry.

Reference

Test Dataset

version: 1

---

Nishang PowershellTCPOneLine

This query detects the Nishang Invoke-PowerShellTCPOneLine utility that spawns a call back to a remote command and control server. This is a powershell oneliner. In addition, this will capture on the command-line additional utilities used by Nishang. Triage the endpoint and identify any parallel processes that look suspicious. Review the reputation of the remote IP or domain contacted by the powershell process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001
• Last Updated: 2021-03-03

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe OR Processes.process_name=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe (Processes.process=*Net.Sockets.TCPClient* AND Processes.process=*System.Text.ASCIIEncoding*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `nishang_powershelltcponeline_filter`

Associated Analytic Story

• HAFNIUM Group

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives may be present. Filter as needed based on initial analysis.

Reference

• https://github.com/samratashok/nishang/blob/master/Shells/Invoke-PowerShellTcpOneLine.ps1

• https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/

• https://www.microsoft.com/security/blog/2021/03/02/hafnium-targeting-exchange-servers/

• https://blog.rapid7.com/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log

version: 1

---

No Windows Updates in a time frame

This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Updates
• ATT&CK:
• Last Updated: 2017-09-15

details

Search

| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product 
| rename Updates.dest as Host 
| rename Updates.status as "Update Status" 
| rename Updates.vendor_product as Product 
| eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0)  
| `security_content_ctime(lastTime)`  
| search isOutlier=1 
| rename lastTime as "Last Update Time", 
| table Host, "Update Status", Product, "Last Update Time" 
| `no_windows_updates_in_a_time_frame_filter`

Associated Analytic Story

• Monitor for Updates

How To Implement

To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems.

Required field

• _time

• Updates.status

• Updates.vendor_product

• Updates.dest

Kill Chain Phase

Known False Positives

None identified

Reference

Test Dataset

version: 1

---

Ntdsutil Export NTDS

Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit
ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q
This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.003
• Last Updated: 2021-01-28

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `ntdsutil_export_ntds_filter`

Associated Analytic Story

• Credential Dumping

• HAFNIUM Group

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1003.003	NTDS	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil

• https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11)

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

• https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log

version: 1

---

O365 Add App Role Assignment Grant User

This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1136.003
• Last Updated: 2021-01-26

details

Search

`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." 
| stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `o365_add_app_role_assignment_grant_user_filter`

Associated Analytic Story

• Office 365 Detections

• Cloud Federated Credential Abuse

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Workload

• Operation

• Actor{}.ID

• Actor{}.Type

• ActorIpAddress

• dest

• ResultStatus

ATT&CK

ID	Technique	Tactic
T1136.003	Cloud Account	Persistence

Kill Chain Phase

• Actions on Objective

Known False Positives

The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.

Reference

• https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

• https://us-cert.cisa.gov/ncas/alerts/aa21-008a

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json

version: 1

---

O365 Added Service Principal

This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1136.003
• Last Updated: 2021-01-26

details

Search

`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." 
| stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `o365_added_service_principal_filter`

Associated Analytic Story

• Office 365 Detections

• Cloud Federated Credential Abuse

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Workload

• signature

• Actor{}.ID

• ModifiedProperties{}.Name

• ModifiedProperties{}.NewValue

• Target{}.ID

• ActorIpAddress

ATT&CK

ID	Technique	Tactic
T1136.003	Cloud Account	Persistence

Kill Chain Phase

• Actions on Objective

Known False Positives

The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.

Reference

• https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

• https://us-cert.cisa.gov/ncas/alerts/aa21-008a

• https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html

• https://www.sygnia.co/golden-saml-advisory

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json

version: 1

---

O365 Bypass MFA via Trusted IP

This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1562.007
• Last Updated: 2021-01-12

details

Search

`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy 
| rex max_match=100 field=ModifiedProperties{}.NewValue "(?<ip_addresses_new_added>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" 
| rex max_match=100 field=ModifiedProperties{}.OldValue "(?<ip_addresses_old>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" 
| eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") 
| mvexpand ip_addresses_new_added 
| where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) 
|stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature vendor_product vendor_account status user_id action 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `o365_bypass_mfa_via_trusted_ip_filter`

Associated Analytic Story

• Office 365 Detections

How To Implement

You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• signature

• ModifiedProperties{}.Name

• ModifiedProperties{}.NewValue

• ModifiedProperties{}.OldValue

• user

• vendor_product

• vendor_account

• status

• user_id

• action

ATT&CK

ID	Technique	Tactic
T1562.007	Disable or Modify Cloud Firewall	Defense Evasion

Kill Chain Phase

• Actions on Objective

Known False Positives

Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration.

Reference

• https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf

• https://attack.mitre.org/techniques/T1562/007/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json

version: 1

---

O365 Disable MFA

This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1556
• Last Updated: 2020-12-16

details

Search

`o365_management_activity` Operation="Disable Strong Authentication." 
| stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `o365_disable_mfa_filter`

Associated Analytic Story

• Office 365 Detections

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Operation

• UserType

• user

• status

• signature

• dest

• ResultStatus

ATT&CK

ID	Technique	Tactic
T1556	Modify Authentication Process	Credential Access, Defense Evasion, Persistence

Kill Chain Phase

• Actions on Objective

Known False Positives

Unless it is a special case, it is uncommon to disable MFA or Strong Authentication

Reference

• https://attack.mitre.org/techniques/T1556/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json

version: 1

---

O365 Excessive Authentication Failures Alert

This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1110
• Last Updated: 2020-12-16

details

Search

`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed 
| stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user 
| where count > 10 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `o365_excessive_authentication_failures_alert_filter`

Associated Analytic Story

• Office 365 Detections

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Workload

• UserAuthenticationMethod

• status

• UserAgent

• src_ip

• user

ATT&CK

ID	Technique	Tactic
T1110	Brute Force	Credential Access

Kill Chain Phase

• Not Applicable

Known False Positives

The threshold for alert is above 10 attempts and this should reduce the number of false positives.

Reference

• https://attack.mitre.org/techniques/T1110/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json

version: 1

---

O365 Excessive SSO logon errors

This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1556
• Last Updated: 2021-01-26

details

Search

`o365_management_activity`  Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired 
| stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId 
| where count > 5 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `o365_excessive_sso_logon_errors_filter`

Associated Analytic Story

• Office 365 Detections

• Cloud Federated Credential Abuse

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Workload

• LogonError

• ActorIpAddress

• UserAgent

• UserId

ATT&CK

ID	Technique	Tactic
T1556	Modify Authentication Process	Credential Access, Defense Evasion, Persistence

Kill Chain Phase

• Actions on Objective

Known False Positives

Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack.

Reference

• https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json

version: 1

---

O365 New Federated Domain Added

This search detects the addition of a new Federated domain.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1136.003
• Last Updated: 2021-01-26

details

Search

`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" 
| stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `o365_new_federated_domain_added_filter`

Associated Analytic Story

• Office 365 Detections

• Cloud Federated Credential Abuse

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity.

Required field

• _time

• Workload

• Operation

• Parameters{}.Value

• ObjectId

• OrganizationName

• OriginatingServer

• UserId

• UserKey

ATT&CK

ID	Technique	Tactic
T1136.003	Cloud Account	Persistence

Kill Chain Phase

• Actions on Objective

Known False Positives

The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider.

Reference

• https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

• https://us-cert.cisa.gov/ncas/alerts/aa21-008a

• https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html

• https://www.sygnia.co/golden-saml-advisory

• https://o365blog.com/post/aadbackdoor/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json

version: 1

---

O365 PST export alert

This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1114
• Last Updated: 2020-12-16

details

Search

`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" 
| stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `o365_pst_export_alert_filter`

Associated Analytic Story

• Office 365 Detections

• Data Exfiltration

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Category

• Name

• Source

• Severity

• AlertEntityId

• Operation

ATT&CK

ID	Technique	Tactic
T1114	Email Collection	Collection

Kill Chain Phase

• Actions on Objective

Known False Positives

PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored.

Reference

• https://attack.mitre.org/techniques/T1114/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json

version: 1

---

O365 Suspicious Admin Email Forwarding

This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1114.003
• Last Updated: 2020-12-16

details

Search

`o365_management_activity` Operation=Set-Mailbox 
| spath input=Parameters 
| rename Identity AS src_user 
| search ForwardingAddress=* 
| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress 
| where count_src_user > 1 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
|`o365_suspicious_admin_email_forwarding_filter`

Associated Analytic Story

• Office 365 Detections

• Data Exfiltration

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Operation

• Parameters

ATT&CK

ID	Technique	Tactic
T1114.003	Email Forwarding Rule	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

unknown

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json

version: 1

---

O365 Suspicious Rights Delegation

This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1114.002
• Last Updated: 2020-12-15

details

Search

`o365_management_activity` Operation=Add-MailboxPermission 
| spath input=Parameters 
| rename User AS src_user, Identity AS dest_user 
| search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf 
| stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
|`o365_suspicious_rights_delegation_filter`

Associated Analytic Story

• Office 365 Detections

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Operation

• Parameters

ATT&CK

ID	Technique	Tactic
T1114.002	Remote Email Collection	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

Service Accounts

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json

version: 1

---

O365 Suspicious User Email Forwarding

This search detects when multiple user configured a forwarding rule to the same destination.

• Product: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1114.003
• Last Updated: 2020-12-16

details

Search

`o365_management_activity` Operation=Set-Mailbox 
| spath input=Parameters 
| rename Identity AS src_user 
| search ForwardingSmtpAddress=* 
| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress 
| where count_src_user > 1 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
|`o365_suspicious_user_email_forwarding_filter`

Associated Analytic Story

• Office 365 Detections

• Data Exfiltration

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Required field

• _time

• Operation

• Parameters

ATT&CK

ID	Technique	Tactic
T1114.003	Email Forwarding Rule	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

unknown

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json

version: 1

---

Office Application Spawn rundll32 process

this detection was designed to identifies suspicious spawned process of known MS office application due to macro or malicious code. this technique can be seen in so many malware like trickbot that used MS office as its weapon or attack vector to initially infect the machines.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-13

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe") Processes.process_name=rundll32.exe  by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `office_application_spawn_rundll32_process_filter`

Associated Analytic Story

• Spearphishing Attachments

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• Processes.process

• Processes.parent_process_name

• _time

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://any.run/malware-trends/trickbot

• https://any.run/report/47561b4e949041eff0a0f4693c59c81726591779fe21183ae9185b5eb6a69847/aba3722a-b373-4dae-8273-8730fb40cdbe

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log

version: 1

---

Office Document Creating Schedule Task

this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the Command to be executed, Task Name, Author, Enabled, and whether it is Hidden or not. schtasks.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -taskschd.dll. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-14

details

Search

`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded = "*\\taskschd.dll" 
| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `office_document_creating_schedule_task_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.

Required field

• ImageLoaded

• AllImageLoaded

• Computer

• EventCode

• Image

• process_name

• ProcessId

• ProcessGuid

• _time

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/

• https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log

version: 1

---

Office Document Executing Macro Code

this detection was designed to identifies suspicious office documents that using macro code. Macro code is known to be one of the prevalent weaponization or attack vector of threat actor. This malicious macro code is embed to a office document as an attachment that may execute malicious payload, download malware payload or other malware component. It is really good practice to disable macro by default to avoid automatically execute macro code while opening or closing a office document files.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-14

details

Search

`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL") 
| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `office_document_executing_macro_code_filter`

Associated Analytic Story

• Spearphishing Attachments

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.

Required field

• ImageLoaded

• AllImageLoaded

• Computer

• EventCode

• Image

• process_name

• ProcessId

• ProcessGuid

• _time

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

Normal Office Document macro use for automation

Reference

• https://www.joesandbox.com/analysis/386500/0/html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log

version: 1

---

Office Document Spawned Child Process To Download

this search is to detect potential malicious office document executing lolbin child process to download payload or other malware. Since most of the attacker abused the capability of office document to execute living on land application to blend it to the normal noise in the infected machine to cover its track.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-16

details

Search

`sysmon` EventCode=1 parent_process_name IN ("powerpnt.exe", "winword.exe", "excel.exe", "visio.exe") process_name = "*.exe" cmdline IN ("*http:*","*https:*")  NOT(OriginalFileName IN("*\\firefox.exe", "*\\chrome.exe","*\\iexplore.exe","*\\msedge.exe")) 
| stats min(_time) as firstTime max(_time) as lastTime count by parent_process_name process_name parent_process cmdline process_id OriginalFileName ProcessGuid Computer EventCode 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `office_document_spawned_child_process_to_download_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances office application and browser may be used.

Required field

• _time

• parent_process_name

• process_name

• parent_process

• cmdline

• process_id

• OriginalFileName

• ProcessGuid

• Computer

• EventCode

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

default browser not in the filter list

Reference

• https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/#

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets2/windows-sysmon.log

version: 1

---

Office Product Spawning BITSAdmin

The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning bitsadmin.exe. In malicious instances, the command-line of bitsadmin.exe will contain a URL to a remote destination or similar command-line arguments as transfer, Download, priority, Foreground. In addition, Threat Research has released a detections identifying suspicious use of bitsadmin.exe. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or bitsadmin.exe will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=bitsadmin.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_bitsadmin_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

No false positives known. Filter as needed.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log

version: 1

---

Office Product Spawning CertUtil

The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning certutil.exe. In malicious instances, the command-line of certutil.exe will contain a URL to a remote destination. In addition, Threat Research has released a detections identifying suspicious use of certutil.exe. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or certutil.exe will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=certutil.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_certutil_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

No false positives known. Filter as needed.

Reference

• https://redcanary.com/threat-detection-report/threats/TA551/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1105/T1105.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log

version: 1

---

Office Product Spawning MSHTA

The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning mshta.exe. In malicious instances, the command-line of mshta.exe will contain the hta file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of mshta.exe. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or mshta.exe will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=mshta.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_mshta_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

No false positives known. Filter as needed.

Reference

• https://redcanary.com/threat-detection-report/threats/TA551/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log

version: 1

---

Office Product Spawning Rundll32 with no DLL

The following detection identifies the latest behavior utilized by IcedID malware family. This detection identifies any Windows Office Product spawning rundll32.exe without a .dll file extension. In malicious instances, the command-line of rundll32.exe will look like rundll32 ..\oepddl.igk2,DllRegisterServer. In addition, Threat Research has released a detection identifying the use of DllRegisterServer on the command-line of rundll32.exe. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze the DLL that was dropped to disk. The Office Product will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=rundll32.exe (Processes.process!=*.dll*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_rundll32_with_no_dll_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

False positives should be limited, but if any are present, filter as needed.

Reference

• https://www.joesandbox.com/analysis/395471/0/html

• https://app.any.run/tasks/cef4b8ba-023c-4b3b-b2ef-6486a44f6ed9/

• https://any.run/malware-trends/icedid

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_icedid.log

version: 1

---

Office Product Spawning Wmic

The following detection identifies the latest behavior utilized by Ursnif malware family. This detection identifies any Windows Office Product spawning wmic.exe. In malicious instances, the command-line of wmic.exe will contain wmic process call create. In addition, Threat Research has released a detection identifying the use of wmic process call create on the command-line of wmic.exe. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or wmic.exe will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=wmic.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_wmic_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

No false positives known. Filter as needed.

Reference

• https://app.any.run/tasks/fb894ab8-a966-4b72-920b-935f41756afd/

• https://attack.mitre.org/techniques/T1047/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1047/T1047.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log

version: 1

---

Okta Account Lockout Events

Detect Okta user lockout events

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.001
• Last Updated: 2020-07-21

details

Search

`okta` displayMessage="Max sign in attempts exceeded" 
| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city 
| table _time, user, country, state, city, src_ip 
| `okta_account_lockout_events_filter` 

Associated Analytic Story

• Suspicious Okta Activity

How To Implement

This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

Required field

• _time

• displayMessage

• client.geographicalContext.country

• client.geographicalContext.state

• client.geographicalContext.city

ATT&CK

ID	Technique	Tactic
T1078.001	Default Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor.

Reference

Test Dataset

version: 2

---

Okta Failed SSO Attempts

Detect failed Okta SSO events

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.001
• Last Updated: 2020-07-21

details

Search

`okta` displayMessage="User attempted unauthorized access to app" 
| stats  min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `okta_failed_sso_attempts_filter` 

Associated Analytic Story

• Suspicious Okta Activity

How To Implement

This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

Required field

• _time

• displayMessage

• app

• user

• result

• src_ip

ATT&CK

ID	Technique	Tactic
T1078.001	Default Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

There may be a faulty config preventing legitmate users from accessing apps they should have access to.

Reference

Test Dataset

version: 2

---

Okta User Logins From Multiple Cities

This search detects logins from the same user from different cities in a 24 hour period.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078.001
• Last Updated: 2020-07-21

details

Search

`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null 
| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `okta_user_logins_from_multiple_cities_filter` 
| search locations > 1

Associated Analytic Story

• Suspicious Okta Activity

How To Implement

This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

Required field

• _time

• displayMessage

• client.geographicalContext.city

• client.geographicalContext.state

• user

ATT&CK

ID	Technique	Tactic
T1078.001	Default Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

Known False Positives

Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should not be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint.

Reference

Test Dataset

version: 2

---

Open Redirect in Splunk Web

This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2017-09-19

details

Search

index=_internal sourcetype=splunk_web_access return_to="/%09/*" 
| `open_redirect_in_splunk_web_filter`

Associated Analytic Story

• Splunk Enterprise Vulnerability

How To Implement

No extra steps needed to implement this search.

Required field

• _time

Kill Chain Phase

• Delivery

Known False Positives

None identified

Reference

Test Dataset

version: 1

---

Osquery pack - ColdRoot detection

This search looks for ColdRoot events from the osx-attacks osquery pack.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2019-01-29

details

Search

| from datamodel Alerts.Alerts 
| search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) 
| rename columns.path as path 
| bucket _time span=30s 
| stats count(path) by _time, host, user, path 
| `osquery_pack___coldroot_detection_filter`

Associated Analytic Story

• ColdRoot MacOS RAT

How To Implement

In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the osx-attacks.conf pack enabled. Also the TA-OSquery must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model

Required field

• _time

Kill Chain Phase

• Installation

• Command and Control

Known False Positives

There are no known false positives.

Reference

Test Dataset

version: 1

---

Overwriting Accessibility Binaries

Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1546.008
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `overwriting_accessibility_binaries_filter`

Associated Analytic Story

• Windows Privilege Escalation

How To Implement

You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

• Filesystem.dest

• Filesystem.file_path

• Filesystem.file_name

• Filesystem.dest

ATT&CK

ID	Technique	Tactic
T1546.008	Accessibility Features	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log

version: 4

---

Phishing Email Detection by Machine Learning Method - SSA

Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1566
• Last Updated: 2020-08-25

details

Search

| from read_ssa_enriched_events() 
| eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), "                                                                                                                                "), _time=map_get(input_event, "_time") 
| where eventLine IS NOT NULL 
| eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, "
|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) 
| apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" 
| eval probability = mvindex(ml_out, 0) 
| where probability > 0.5 
| eval start_time=_time, end_time=_time, entities="TBD", body="TBD" 
| select probability, body, entities, start_time, end_time 
| into write_ssa_detected_events();

Associated Analytic Story

How To Implement

Events are fed to DSP contains at least email's sender, subject and its message body.

Required field

ATT&CK

ID	Technique	Tactic
T1566	Phishing	Initial Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85%

Reference

Test Dataset

version: 1

---

Plain HTTP POST Exfiltrated Data

This search is to detect potential plain HTTP POST method data exfiltration. This network traffic is commonly used by trickbot, trojanspy, keylogger or APT adversary where arguments or commands are sent in plain text to the remote C2 server using HTTP POST method as part of data exfiltration.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1048.003
• Last Updated: 2021-04-22

details

Search

`stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*", "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") 
|stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `plain_http_post_exfiltrated_data_filter`

Associated Analytic Story

• Command and Control

How To Implement

To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled.

Required field

• _time

• http_method

• http_user_agent

• uri_path

• url

• bytes_in

• bytes_out

ATT&CK

ID	Technique	Tactic
T1048.003	Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol	Exfiltration

Kill Chain Phase

• Exfiltration

Known False Positives

unknown

Reference

• https://blog.talosintelligence.com/2020/03/trickbot-primer.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/plain_exfil_data/stream_http_events.log

version: 1

---

PowerShell Start-BitsTransfer

Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the -TransferType to Upload for exfiltration of files. In an instance where Upload is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation?

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1197
• Last Updated: 2021-03-29

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*start-bitstransfer* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_start_bitstransfer_filter`

Associated Analytic Story

• BITS Jobs

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1197	BITS Jobs	Defense Evasion, Persistence

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives. It is possible administrators will utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent process or command-line arguments.

Reference

• https://isc.sans.edu/diary/Investigating+Microsoft+BITS+Activity/23281

• https://docs.microsoft.com/en-us/windows/win32/bits/using-windows-powershell-to-create-bits-transfer-jobs

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log

version: 1

---

Powershell Remote Thread To Known Windows Process

this search is designed to detect suspicious powershell process that tries to inject code and to known/critical windows process and execute it using CreateRemoteThread. This technique is seen in several malware like trickbot and offensive tooling like cobaltstrike where it load a shellcode to svchost.exe to execute reverse shell to c2 and download another payload

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1055
• Last Updated: 2021-04-19

details

Search

`sysmon` EventCode = 8 process_name IN ("powershell_ise.exe", "powershell.exe") TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe") 
| stats  min(_time) as firstTime max(_time) as lastTime count by SourceImage process_name SourceProcessId SourceProcessGuid TargetImage TargetProcessId NewThreadId StartAddress Computer EventCode 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_remote_thread_to_known_windows_process_filter`

Associated Analytic Story

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, Create Remote thread from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of create remote thread may be used.

Required field

• _time

• SourceImage

• process_name

• SourceProcessId

• SourceProcessGuid

• TargetImage

• TargetProcessId

• NewThreadId

• StartAddress

• Computer

• EventCode

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://thedfirreport.com/2021/01/11/trickbot-still-alive-and-well/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log

version: 1

---

Probing Access with Stolen Credentials via PowerSploit modules

This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1078, T1098
• Last Updated: 2020-11-04

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Privilege Escalation

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_user_id

• dest_device_id

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Process Creating LNK file in Suspicious Location

This search looks for a process launching an *.lnk file under C:\User* or *\Local\Temp\*. This is common behavior used by various spear phishing tools.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1566.002
• Last Updated: 2021-01-28

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*"  by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user 
| `drop_dm_object_name(Filesystem)` 
| rename process_id as lnk_pid 
| join lnk_pid, _time [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*  by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process 
| `drop_dm_object_name(Processes)` 
| rename parent_process_id as lnk_pid 
| fields _time lnk_pid process_id dest process_name process_path process] 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash 
| `process_creating_lnk_file_in_suspicious_location_filter` 

Associated Analytic Story

• Spearphishing Attachments

How To Implement

You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.

Required field

• _time

• Filesystem.file_name

• Filesystem.file_path

• Filesystem.process_id

• Filesystem.file_name

• Filesystem.file_path

• Filesystem.file_hash

• Filesystem.user

ATT&CK

ID	Technique	Tactic
T1566.002	Spearphishing Link	Initial Access

Kill Chain Phase

• Installation

• Actions on Objectives

Known False Positives

This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories.

Reference

• https://attack.mitre.org/techniques/T1566/001/

• https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log

version: 4

---

Process Deleting Its Process File Path

This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.002
• Last Updated: 2021-03-17

details

Search

`sysmon` EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" 
|eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") 
| stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result 
| where result = "Found" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `process_deleting_its_process_file_path_filter`

Associated Analytic Story

• Clop Ransomware

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• EventCode

• Computer

• user

• ParentImage

• ParentCommandLine

• Image

• cmdline

• ProcessID

• result

• _time

ATT&CK

ID	Technique	Tactic
T1003.002	Security Account Manager	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

• https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log

version: 1

---

Process Execution via WMI

This search looks for processes launched via WMI.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1047
• Last Updated: 2020-03-16

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name  
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `process_execution_via_wmi_filter` 

Associated Analytic Story

• Suspicious WMI Use

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.parent_process_name

• Processes.user

• Processes.dest

• Processes.process_name

ATT&CK

ID	Technique	Tactic
T1047	Windows Management Instrumentation	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, administrators may use wmi to execute commands for legitimate purposes.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log

version: 3

---

Process Kill Base On File Path

The following analytic identifies the use of wmic.exe using delete to remove a executable path. This is typically ran via a batch file during beginning stages of an adversary setting up for mining on an endpoint.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-05-04

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where   Processes.process_name = "wmic.exe" AND Processes.process="*process*" AND Processes.process="*executablepath*" AND Processes.process="*delete*" by  Processes.parent_process_name Processes.process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `process_kill_base_on_file_path_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed wmic.exe may be used.

Required field

• _time

• Processes.parent_process_name

• Processes.process_name

• Processes.dest

• Processes.user

• Processes.process

• Processes.process_id

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Unknown.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Processes Tapping Keyboard Events

This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2019-01-25

details

Search

| from datamodel Alerts.Alerts 
| search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps 
| rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id
| dedup host,process_name 
| table host,process_name, cmd, process_id 
| `processes_tapping_keyboard_events_filter`

Associated Analytic Story

• ColdRoot MacOS RAT

How To Implement

In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the osx-attacks.conf pack enabled. Also the TA-OSquery must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model.

Required field

• _time

• app

• name

• columns.cmdline

• columns.name

• columns.pid

• host

Kill Chain Phase

• Command and Control

Known False Positives

There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see this comment.

Reference

Test Dataset

version: 1

---

Processes created by netsh

This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.004
• Last Updated: 2020-11-23

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `processes_created_by_netsh_filter`

Associated Analytic Story

• Netsh Abuse

How To Implement

To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1562.004	Disable or Modify System Firewall	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft.

Reference

Test Dataset

version: 5

---

Processes launching netsh

This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.004
• Last Updated: 2020-07-10

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest 
|`drop_dm_object_name("Processes")` 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
|`processes_launching_netsh_filter`

Associated Analytic Story

• Netsh Abuse

• Disabling Security Tools

• DHS Report TA18-074A

How To Implement

To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model

Required field

• _time

• Processes.process

• Processes.parent_process_name

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1562.004	Disable or Modify System Firewall	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log

version: 3

---

Prohibited Network Traffic Allowed

This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1048
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action 
| lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport 
| search is_prohibited=true 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Traffic")` 
| `prohibited_network_traffic_allowed_filter`

Associated Analytic Story

• Prohibited Traffic Allowed or Protocol Mismatch

• Ransomware

• Command and Control

How To Implement

In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated.

Required field

• _time

• All_Traffic.action

• All_Traffic.src_ip

• All_Traffic.dest_ip

• All_Traffic.dest_port

ATT&CK

ID	Technique	Tactic
T1048	Exfiltration Over Alternative Protocol	Exfiltration

Kill Chain Phase

• Delivery

• Command and Control

Known False Positives

None identified

Reference

Test Dataset

version: 2

---

Prohibited Software On Endpoint

This search looks for applications on the endpoint that you have marked as prohibited.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK:
• Last Updated: 2019-10-11

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| `prohibited_softwares` 
| `prohibited_software_on_endpoint_filter`

Associated Analytic Story

• Monitor for Unauthorized Software

• Emotet Malware DHS Report TA18-201A

• SamSam Ransomware

How To Implement

To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the process_name (not the entire process path) marked as "prohibited" in the Enterprise Security interesting processes table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security.

Required field

• _times

Kill Chain Phase

• Installation

• Command and Control

• Actions on Objectives

Known False Positives

None identified

Reference

Test Dataset

version: 2

---

Protocol or Port Mismatch

This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1048.003
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port 
|`security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Traffic")` 
| `protocol_or_port_mismatch_filter`

Associated Analytic Story

• Prohibited Traffic Allowed or Protocol Mismatch

• Command and Control

How To Implement

Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports.

Required field

• _time

• All_Traffic.app

• All_Traffic.dest_port

• All_Traffic.src_ip

• All_Traffic.dest_ip

ATT&CK

ID	Technique	Tactic
T1048.003	Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol	Exfiltration

Kill Chain Phase

• Command and Control

Known False Positives

None identified

Reference

Test Dataset

version: 2

---

Protocols passing authentication in cleartext

This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK:
• Last Updated: 2020-11-04

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Traffic")` 
| `protocols_passing_authentication_in_cleartext_filter`

Associated Analytic Story

• Use of Cleartext Protocols

How To Implement

This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model.

Required field

• _time

• All_Traffic.transport

• All_Traffic.dest_port

• All_Traffic.user

• All_Traffic.src

• All_Traffic.dest

Kill Chain Phase

• Reconnaissance

• Actions on Objectives

Known False Positives

Some networks may use kerberized FTP or telnet servers, however, this is rare.

Reference

Test Dataset

version: 2

---

Ransomware Notes bulk creation

The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry or the ransomware note filename is not in your ransomware lookup table list for monitoring.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1486
• Last Updated: 2021-03-12

details

Search

`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") 
|bin _time span=10s 
| stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name 
| where unique_readme_path_count >= 15 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `ransomware_notes_bulk_creation_filter`

Associated Analytic Story

• Clop Ransomware

• DarkSide Ransomware

How To Implement

You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• EventCode

• file_name

• _time

• TargetFilename

• Computer

• Image

• user

ATT&CK

ID	Technique	Tactic
T1486	Data Encrypted for Impact	Impact

Kill Chain Phase

• Obfuscation

Known False Positives

unknown

Reference

• https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

• https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log

version: 1

---

Rare Parent-Child Process Relationship

An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1203, T1059, T1053, T1072
• Last Updated: 2020-08-13

details

Search

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) 
| where parent_process_name!=null 
| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id 
| conditional_anomaly conditional="parent_process_name" target="process_name" 
| rename output as input 
| where input < 1 
| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L 
| where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe")

| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD" 
| into write_null();

Associated Analytic Story

• Unusual Processes

How To Implement

Collect endpoint data such as sysmon or 4688 events.

Required field

• process_name

• parent_process_name

• _time

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1203	Exploitation for Client Execution	Execution
T1059	Command and Scripting Interpreter	Execution
T1053	Scheduled Task/Job	Execution, Persistence, Privilege Escalation
T1072	Software Deployment Tools	Execution, Lateral Movement

Kill Chain Phase

• Exploitation

Known False Positives

Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline.

Reference

Test Dataset

version: 1

---

Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules

This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1078, T1087, T1484
• Last Updated: 2020-11-05

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1087	Account Discovery	Discovery
T1484	Domain Policy Modification	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance and Access to Accounts and Groups via Mimikatz modules

This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1078, T1087, T1484
• Last Updated: 2020-11-05

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1087	Account Discovery	Discovery
T1484	Domain Policy Modification	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

version: 1

---

Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules

This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1199, T1482, T1590, T1591, T1595
• Last Updated: 2020-11-06

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1199	Trusted Relationship	Initial Access
T1482	Domain Trust Discovery	Discovery
T1590	Gather Victim Network Information	Reconnaissance
T1591	Gather Victim Org Information	Reconnaissance
T1595	Active Scanning	Reconnaissance

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance and Access to Computers and Domains via PowerSploit modules

This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1592, T1590, T1087
• Last Updated: 2020-11-06

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1592	Gather Victim Host Information	Reconnaissance
T1590	Gather Victim Network Information	Reconnaissance
T1087	Account Discovery	Discovery

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance and Access to Computers via Mimikatz modules

This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1592
• Last Updated: 2020-11-06

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1592	Gather Victim Host Information	Reconnaissance

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

version: 1

---

Reconnaissance and Access to Operating System Elements via PowerSploit modules

This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1007, T1012, T1046, T1047, T1057, T1083, T1518, T1592.002
• Last Updated: 2020-11-06

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainProcess/)=true OR match_regex(cmd_line, /(?i)Invoke-ProcessHunter/)=true OR match_regex(cmd_line, /(?i)Get-ServiceDetail/)=true OR match_regex(cmd_line, /(?i)Get-WMIProcess/)=true OR match_regex(cmd_line, /(?i)Get-NetProcess/)=true OR match_regex(cmd_line, /(?i)Get-SecurityPackage/)=true OR match_regex(cmd_line, /(?i)Find-DomainObjectPropertyOutlier/)=true OR match_regex(cmd_line, /(?i)Get-DomainObject/)=true OR match_regex(cmd_line, /(?i)Get-ADObject/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegMountedDrive/)=true OR match_regex(cmd_line, /(?i)Get-RegistryMountedDrive/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1007	System Service Discovery	Discovery
T1012	Query Registry	Discovery
T1046	Network Service Scanning	Discovery
T1047	Windows Management Instrumentation	Execution
T1057	Process Discovery	Discovery
T1083	File and Directory Discovery	Discovery
T1518	Software Discovery	Discovery
T1592.002	Software	Reconnaissance

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance and Access to Processes and Services via Mimikatz modules

This detection identifies use of Mimikatz modules for discovery and access to services and processes.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1007, T1046, T1057
• Last Updated: 2020-11-06

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1007	System Service Discovery	Discovery
T1046	Network Service Scanning	Discovery
T1057	Process Discovery	Discovery

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

version: 1

---

Reconnaissance and Access to Shared Resources via Mimikatz modules

This detection identifies use of Mimikatz modules for discovery and access to network shares.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1021.002, T1135, T1039
• Last Updated: 2020-11-06

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1021.002	SMB/Windows Admin Shares	Lateral Movement
T1135	Network Share Discovery	Discovery
T1039	Data from Network Shared Drive	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

version: 1

---

Reconnaissance and Access to Shared Resources via PowerSploit modules

This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1021.002, T1135, T1039
• Last Updated: 2020-11-06

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1021.002	SMB/Windows Admin Shares	Lateral Movement
T1135	Network Share Discovery	Discovery
T1039	Data from Network Shared Drive	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance of Access and Persistence Opportunities via PowerSploit modules

This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1053, T1068, T1078, T1543, T1547, T1574
• Last Updated: 2020-11-05

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1053	Scheduled Task/Job	Execution, Persistence, Privilege Escalation
T1068	Exploitation for Privilege Escalation	Privilege Escalation
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1543	Create or Modify System Process	Persistence, Privilege Escalation
T1547	Boot or Logon Autostart Execution	Persistence, Privilege Escalation
T1574	Hijack Execution Flow	Defense Evasion, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance of Connectivity via PowerSploit modules

This detection identifies access to PowerSploit modules for reconnaissance of connectivity.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1021.002, T1135, T1039
• Last Updated: 2020-11-06

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1021.002	SMB/Windows Admin Shares	Lateral Movement
T1135	Network Share Discovery	Discovery
T1039	Data from Network Shared Drive	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance of Credential Stores and Services via Mimikatz modules

This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1589.001, T1590.001, T1590.003, T1068, T1078, T1098
• Last Updated: 2020-11-03

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1589.001	Credentials	Reconnaissance
T1590.001	Domain Properties	Reconnaissance
T1590.003	Network Trust Dependencies	Reconnaissance
T1068	Exploitation for Privilege Escalation	Privilege Escalation
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

version: 1

---

Reconnaissance of Defensive Tools via PowerSploit modules

This detection identifies use of PowerSploit modules for assessment of presence of defensive tools.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1595.002, T1592.002
• Last Updated: 2020-11-05

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1595.002	Vulnerability Scanning	Reconnaissance
T1592.002	Software	Reconnaissance

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules

This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1068, T1078, T1098
• Last Updated: 2020-11-05

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

version: 1

---

Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules

This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1543, T1055, T1574
• Last Updated: 2020-11-05

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Discovery Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• _time

• process

• dest_device_id

• dest_user_id

ATT&CK

ID	Technique	Tactic
T1543	Create or Modify System Process	Persistence, Privilege Escalation
T1055	Process Injection	Defense Evasion, Privilege Escalation
T1574	Hijack Execution Flow	Defense Evasion, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

• https://en.wikipedia.org/wiki/Microsoft_Detours

Test Dataset

version: 1

---

Reg exe Manipulating Windows Services Registry Keys

The search looks for reg.exe modifying registry keys that define Windows services and their configurations.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1574.011
• Last Updated: 2020-11-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `reg_exe_manipulating_windows_services_registry_keys_filter`

Associated Analytic Story

• Windows Service Abuse

• Windows Persistence Techniques

How To Implement

To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.

Required field

• _time

• Processes.process_name

• Processes.parent_process_name

• Processes.user

• Processes.process

• Processes.process_id

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1574.011	Services Registry Permissions Weakness	Defense Evasion, Persistence, Privilege Escalation

Kill Chain Phase

• Installation

Known False Positives

It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log

version: 5

---

Reg exe used to hide files directories via registry keys

The search looks for command-line arguments used to hide a file or directory using the reg add command.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1564.001
• Last Updated: 2019-02-27

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)`
| regex process = "(/d\s+2)" 
| `reg_exe_used_to_hide_files_directories_via_registry_keys_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

• Suspicious Windows Registry Activities

• Windows Persistence Techniques

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1564.001	Hidden Files and Directories	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

None at the moment

Reference

Test Dataset

version: 2

---

Registry Keys Used For Persistence

The search looks for modifications to registry keys that can be used to launch an application or service at system startup.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1547.001
• Last Updated: 2020-11-27

details

Search

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `registry_keys_used_for_persistence_filter`

Associated Analytic Story

• Suspicious Windows Registry Activities

• Suspicious MSHTA Activity

• DHS Report TA18-074A

• Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

• Ransomware

• Windows Persistence Techniques

• Emotet Malware DHS Report TA18-201A

How To Implement

To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.dest

• Registry.user

ATT&CK

ID	Technique	Tactic
T1547.001	Registry Run Keys / Startup Folder	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log

version: 5

---

Registry Keys Used For Privilege Escalation

This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1546.012
• Last Updated: 2020-11-27

details

Search

| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name 
| `security_content_ctime(lastTime)`  
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `registry_keys_used_for_privilege_escalation_filter`

Associated Analytic Story

• Windows Privilege Escalation

• Suspicious Windows Registry Activities

• Cloud Federated Credential Abuse

How To Implement

To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.

Required field

• _time

• Registry.registry_path

• Registry.registry_key_name

• Registry.dest

• Registry.user

ATT&CK

ID	Technique	Tactic
T1546.012	Image File Execution Options Injection	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task.

Reference

• https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log

version: 4

---

Registry Keys for Creating SHIM Databases

This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1546.011
• Last Updated: 2020-11-26

details

Search

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `registry_keys_for_creating_shim_databases_filter`

Associated Analytic Story

• Suspicious Windows Registry Activities

• Windows Persistence Techniques

How To Implement

To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_path

• Registry.dest

• Registry.user

ATT&CK

ID	Technique	Tactic
T1546.011	Application Shimming	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log

version: 3

---

Remote Desktop Network Bruteforce

This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1021.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
| eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 
| where count>(avg + stdev*2) 
| rename All_Traffic.src AS src All_Traffic.dest AS dest 
| table firstTime lastTime src dest count avg p50 stdev 
| `remote_desktop_network_bruteforce_filter`

Associated Analytic Story

• SamSam Ransomware

• Ryuk Ransomware

How To Implement

You must ensure that your network traffic data is populating the Network_Traffic data model.

Required field

• _time

• All_Traffic.app

• All_Traffic.src

• All_Traffic.dest

• All_Traffic.dest_port

ATT&CK

ID	Technique	Tactic
T1021.001	Remote Desktop Protocol	Lateral Movement

Kill Chain Phase

• Reconnaissance

• Delivery

Known False Positives

RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network.

Reference

Test Dataset

version: 2

---

Remote Desktop Network Traffic

This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1021.001
• Last Updated: 2020-07-07

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
| `drop_dm_object_name("All_Traffic")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `remote_desktop_network_traffic_filter` 

Associated Analytic Story

• SamSam Ransomware

• Ryuk Ransomware

• Hidden Cobra Malware

• Lateral Movement

How To Implement

To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups.

Required field

• _time

• All_Traffic.dest_port

• All_Traffic.dest_category

• All_Traffic.src_category

• All_Traffic.src

• All_Traffic.dest

• All_Traffic.dest_port

ATT&CK

ID	Technique	Tactic
T1021.001	Remote Desktop Protocol	Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

Remote Desktop may be used legitimately by users on the network.

Reference

Test Dataset

version: 3

---

Remote Desktop Process Running On System

This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the common_rdp_source category in the Assets and Identity framework.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1021.001
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| `remote_desktop_process_running_on_system_filter` 

Associated Analytic Story

• Hidden Cobra Malware

• Lateral Movement

How To Implement

To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups.

Required field

• _time

• Processes.process

• Processes.dest_category

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1021.001	Remote Desktop Protocol	Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

Remote Desktop may be used legitimately by users on the network.

Reference

Test Dataset

version: 5

---

Remote Process Instantiation via WMI

This search looks for wmic.exe being launched with parameters to spawn a process on a remote system.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1047
• Last Updated: 2020-11-30

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*"   by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `remote_process_instantiation_via_wmi_filter`

Associated Analytic Story

• Ransomware

• Suspicious WMI Use

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1047	Windows Management Instrumentation	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log

version: 5

---

Remote Registry Key modifications

This search monitors for remote modifications to registry keys.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-03-02

details

Search

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where  Registry.registry_path="\\\\*"  by Registry.dest , Registry.user 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `remote_registry_key_modifications_filter`

Associated Analytic Story

• Windows Defense Evasion Tactics

• Suspicious Windows Registry Activities

• Windows Persistence Techniques

How To Implement

To successfully implement this search, you must populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right.

Required field

• _time

Kill Chain Phase

• Actions on Objectives

Known False Positives

This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out.

Reference

Test Dataset

version: 3

---

Remote WMI Command Attempt

This search looks for wmic.exe being launched with parameters to operate on remote systems.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1047
• Last Updated: 2018-12-03

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe  AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `remote_wmi_command_attempt_filter`

Associated Analytic Story

• Suspicious WMI Use

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1047	Windows Management Instrumentation	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators may use this legitimately to gather info from remote systems.

Reference

Test Dataset

version: 2

---

Resize ShadowStorage volume

The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum size as much as possible

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1490
• Last Updated: 2021-03-12

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name =  "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `resize_shadowstorage_volume_filter`

Associated Analytic Story

• Clop Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• Processes.process

• Process.parent_process_name

• _time

• Processes.process_name

• Processes.parent_process

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1490	Inhibit System Recovery	Impact

Kill Chain Phase

• Exploitation

Known False Positives

network admin can resize the shadowstorage for valid purposes.

Reference

• https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

• https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log

version: 1

---

Revil Common Exec Parameter

This analytic identifies suspicious commandline parameter that are commonly used by REVIL ransomware to encrypts the compromise machine.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1204
• Last Updated: 2021-06-02

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-nolan*" OR Processes.process = "*-nolocal*" OR Processes.process = "*-fast*" OR Processes.process = "*-full*" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `revil_common_exec_parameter_filter`

Associated Analytic Story

• Ransomware

• Revil Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.parent_process

• Processes.dest

• Processes.user

• Processes.process_id

• Processes.process_guid

ATT&CK

ID	Technique	Tactic
T1204	User Execution	Execution

Kill Chain Phase

• Exploitation

Known False Positives

third party tool may have same command line parameters as revil ransomware.

Reference

• https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

• https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log

version: 1

---

Revil Registry Entry

This analytic identifies suspicious modification in registry entry to keep some malware data during its infection. This technique seen in several apt implant, malware and ransomware like REVIL where it keep some information like the random generated file extension it uses for all the encrypted files and ransomware notes file name in the compromised host.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1112
• Last Updated: 2021-06-02

details

Search

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" AND (Registry.registry_value_name = "\.*" OR Registry.registry_value_name = "Binary Data") by Registry.registry_value_name Registry.dest Registry.user 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `revil_registry_entry_filter`

Associated Analytic Story

• Ransomware

• Revil Ransomware

How To Implement

to successfully implement this search, you need to be ingesting logs with the Image, TargetObject registry key, registry Details from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Registry.dest

• Registry.user

• Registry.registry_value_name

• Registry.registry_path

• Registry.registry_key_name

ATT&CK

ID	Technique	Tactic
T1112	Modify Registry	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

• https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log

version: 1

---

RunDLL Loading DLL By Ordinal

This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.011
• Last Updated: 2020-11-30

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `rundll_loading_dll_by_ordinal_filter`

Associated Analytic Story

• Unusual Processes

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.parent_process_name

• Processes.process

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion

Kill Chain Phase

• Installation

Known False Positives

While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log

version: 4

---

Rundll32 with no Command Line Arguments with Network

The following analytic identifies rundll32.exe with no command line arguments and performing a network connection. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.011
• Last Updated: 2021-04-19

details

Search

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| regex process="(rundll32\.exe.{0,4}$)" 
| join  process_id [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
| `drop_dm_object_name(Ports)` 
| rename  dest as connection_to_CNC] 
| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port 
| `rundll32_with_no_command_line_arguments_with_network_filter`

Associated Analytic Story

• Suspicious Rundll32 Activity

• Cobalt Strike

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes and port node.

Required field

• _time

• EventID

• process_name

• process_id

• parent_process_name

• dest_port

• process_path

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.

Reference

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32

• https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log

version: 1

---

Ryuk Test Files Detected

The search looks for files that contain the key word Ryuk under any folder in the C drive, which is consistent with Ryuk propagation.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1486
• Last Updated: 2020-11-06

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `ryuk_test_files_detected_filter`

Associated Analytic Story

• Ryuk Ransomware

How To Implement

You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

• Filesystem.file_path

• Filesystem.dest

• Filesystem.user

ATT&CK

ID	Technique	Tactic
T1486	Data Encrypted for Impact	Impact

Kill Chain Phase

• Delivery

Known False Positives

If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log

version: 1

---

Ryuk Wake on LAN Command

This Splunk query identifies the use of Wake-on-LAN utilized by Ryuk ransomware. The Ryuk Ransomware uses the Wake-on-Lan feature to turn on powered off devices on a compromised network to have greater success encrypting them. This is a high fidelity indicator of Ryuk ransomware executing on an endpoint. Upon triage, isolate the endpoint. Additional file modification events will be within the users profile (\appdata\roaming) and in public directories (users\public). Review all Scheduled Tasks on the isolated endpoint and across the fleet. Suspicious Scheduled Tasks will include a path to a unknown binary and those endpoints should be isolated until triaged.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.003
• Last Updated: 2021-03-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*" OR Processes.process="*9 REP*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `ryuk_wake_on_lan_command_filter`

Associated Analytic Story

• Ryuk Ransomware

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1059.003	Windows Command Shell	Execution

Kill Chain Phase

• Exploitation

• Lateral Movement

Known False Positives

Limited to no known false positives.

Reference

• https://www.bleepingcomputer.com/news/security/ryuk-ransomware-uses-wake-on-lan-to-encrypt-offline-devices/

• https://www.bleepingcomputer.com/news/security/ryuk-ransomware-now-self-spreads-to-other-windows-lan-devices/

• https://www.cert.ssi.gouv.fr/uploads/CERTFR-2021-CTI-006.pdf

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/ryuk/windows-sysmon.log

version: 1

---

SLUI RunAs Elevated

The following analytic identifies the Microsoft Software Licensing User Interface Tool, slui.exe, elevating access using the -verb runas function. This particular bypass utilizes a registry key/value. Identified by two sources, the registry keys are HKCU\Software\Classes\exefile\shell and HKCU\Software\Classes\launcher.Systemsettings\Shell\open\command. To simulate this behavior, multiple POC are available. The analytic identifies the use of runas by slui.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1548.002
• Last Updated: 2021-05-13

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=slui.exe (Processes.process=*-verb* Processes.process=*runas*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `slui_runas_elevated_filter`

Associated Analytic Story

• DarkSide Ransomware

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1548.002	Bypass User Account Control	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives should be present as this is not commonly used by legitimate applications.

Reference

• https://www.exploit-db.com/exploits/46998

• https://medium.com/@mattharr0ey/privilege-escalation-uac-bypass-in-changepk-c40b92818d1b

• https://gist.github.com/r00t-3xp10it/0c92cd554d3156fd74f6c25660ccc466

• https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/

• https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log

version: 1

---

SLUI Spawning a Process

The following analytic identifies the Microsoft Software Licensing User Interface Tool, slui.exe, spawning a child process. This behavior is associated with publicly known UAC bypass. slui.exe is commonly associated with software updates and is most often spawned by svchost.exe. The slui.exe process should not have child processes, and any processes spawning from it will be running with elevated privileges. During triage, review the child process and additional parallel processes. Identify any file modifications that may have lead to the bypass.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1548.002
• Last Updated: 2021-05-13

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=slui.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `slui_spawning_a_process_filter`

Associated Analytic Story

• DarkSide Ransomware

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1548.002	Bypass User Account Control	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Certain applications may spawn from slui.exe that are legitimate. Filtering will be needed to ensure proper monitoring.

Reference

• https://www.exploit-db.com/exploits/46998

• https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/

• https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log

version: 1

---

SMB Traffic Spike

This search looks for spikes in the number of Server Message Block (SMB) traffic connections.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1021.002
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src 
| `drop_dm_object_name("All_Traffic")` 
| eventstats max(_time) as maxtime 
| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as stdev by src 
| eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0) 
| where isOutlier=1 
| table src count 
| `smb_traffic_spike_filter` 

Associated Analytic Story

• Emotet Malware DHS Report TA18-201A

• Hidden Cobra Malware

• Ransomware

• DHS Report TA18-074A

How To Implement

This search requires you to be ingesting your network traffic logs and populating the Network_Traffic data model.

Required field

• _time

• All_Traffic.dest_port

• All_Traffic.app

• All_Traffic.src

ATT&CK

ID	Technique	Tactic
T1021.002	SMB/Windows Admin Shares	Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

A file server may experience high-demand loads that could cause this analytic to trigger.

Reference

Test Dataset

version: 3

---

SMB Traffic Spike - MLTK

This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1021.002
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src 
| eval HourOfDay=strftime(_time, "%H") 
| eval DayOfWeek=strftime(_time, "%A") 
| `drop_dm_object_name(All_Traffic)` 
| apply smb_pdfmodel threshold=0.001 
| rename "IsOutlier(count)" as isOutlier 
| search isOutlier > 0 
| sort -count 
| table _time src dest port count 
| `smb_traffic_spike___mltk_filter` 

Associated Analytic Story

• Emotet Malware DHS Report TA18-201A

• Hidden Cobra Malware

• Ransomware

• DHS Report TA18-074A

How To Implement

To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.
This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \

1. Label: Number of events, Field: count
   Detailed documentation on how to create a new field within Incident Review is found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

Required field

• _time

• All_Traffic.dest_ip

• All_Traffic.dest_port

• All_Traffic.app

• All_Traffic.src

ATT&CK

ID	Technique	Tactic
T1021.002	SMB/Windows Admin Shares	Lateral Movement

Kill Chain Phase

• Actions on Objectives

Known False Positives

If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the smb_traffic_spike_mltk_filter macro to filter out false positive results

Reference

Test Dataset

version: 3

---

SQL Injection with Long URLs

This search looks for long URLs that have several SQL commands visible within them.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Web
• ATT&CK: T1190
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent 
| `drop_dm_object_name("Web")` 
| eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 
| where num_sql_cmds > 3 
| `sql_injection_with_long_urls_filter`

Associated Analytic Story

• SQL Injection

How To Implement

To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table.

Required field

• _time

• Web.dest_category

• Web.url_length

• Web.http_user_agent_length

• Web.src

• Web.dest

• Web.url

• Web.http_user_agent

ATT&CK

ID	Technique	Tactic
T1190	Exploit Public-Facing Application	Initial Access

Kill Chain Phase

• Delivery

Known False Positives

It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate.

Reference

Test Dataset

version: 2

---

Samsam Test File Write

The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1486
• Last Updated: 2018-12-14

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `samsam_test_file_write_filter`

Associated Analytic Story

• SamSam Ransomware

How To Implement

You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

• Filesystem.user

• Filesystem.dest

• Filesystem.file_name

• Filesystem.file_path

ATT&CK

ID	Technique	Tactic
T1486	Data Encrypted for Impact	Impact

Kill Chain Phase

• Delivery

Known False Positives

No false positives have been identified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log

version: 1

---

Sc exe Manipulating Windows Services

This search looks for arguments to sc.exe indicating the creation or modification of a Windows service.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1543.003
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `sc_exe_manipulating_windows_services_filter`

Associated Analytic Story

• Windows Service Abuse

• DHS Report TA18-074A

• Orangeworm Attack Group

• Windows Persistence Techniques

• Disabling Security Tools

• NOBELIUM Group

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1543.003	Windows Service	Persistence, Privilege Escalation

Kill Chain Phase

• Installation

Known False Positives

Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log

version: 4

---

Schedule Task with HTTP Command Arguments

The following query utilizes Windows Security EventCode 4698, A scheduled task was created, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the Command to be executed, Task Name, Author, Enabled, and whether it is Hidden or not. schtasks.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -taskschd.dll. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1053
• Last Updated: 2021-04-19

details

Search

`wineventlog_security` EventCode=4698 
| xmlkv Message
| search Arguments IN ("*http*") 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schedule_task_with_http_command_arguments_filter`

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.

Required field

• _time

• dest

• Task_Name

• Command

• Author

• Enabled

• Hidden

• Arguments

ATT&CK

ID	Technique	Tactic
T1053	Scheduled Task/Job	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/tasksched/windows-security.log

version: 1

---

Schedule Task with Rundll32 Command Trigger

The following query utilizes Windows Security EventCode 4698, A scheduled task was created, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the Command to be executed, Task Name, Author, Enabled, and whether it is Hidden or not. schtasks.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -taskschd.dll. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1053
• Last Updated: 2021-04-19

details

Search

`wineventlog_security` EventCode=4698 
| xmlkv Message 
| search Command IN ("*rundll32*") 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schedule_task_with_rundll32_command_trigger_filter`

Associated Analytic Story

• Windows Persistence Techniques

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.

Required field

• _time

• dest

• Task_Name

• Command

• Author

• Enabled

• Hidden

• Arguments

ATT&CK

ID	Technique	Tactic
T1053	Scheduled Task/Job	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://labs.vipre.com/trickbot-and-its-modules/

• https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/tasksched/windows-security.log

version: 1

---

Scheduled Task Deleted Or Created via CMD

This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1053.005
• Last Updated: 2020-12-17

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `scheduled_task_deleted_or_created_via_cmd_filter` 

Associated Analytic Story

• DHS Report TA18-074A

• NOBELIUM Group

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.parent_process_name

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1053.005	Scheduled Task	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Tasks should not be manually created via CLI, this is rarely done by admins as well

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log

version: 5

---

Scheduled tasks used in BadRabbit ransomware

This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1053.005
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process  from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*"  OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| search (process=*rhaegal* OR process=*drogon* OR *viserion_*) 
| `scheduled_tasks_used_in_badrabbit_ransomware_filter`

Associated Analytic Story

• Ransomware

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1053.005	Scheduled Task	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

No known false positives

Reference

Test Dataset

version: 3

---

Schtasks Run Task On Demand

This analytic identifies an on demand run of a Windows Schedule Task through shell or command-line. This technique has been used by adversaries that force to run their created Schedule Task as their persistence mechanism or for lateral movement as part of their malicious attack to the compromised machine.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1053
• Last Updated: 2021-05-07

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/run*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schtasks_run_task_on_demand_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed schtasks.exe may be used.

Required field

• _time

• Processes.process

• Processes.process_id

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1053	Scheduled Task/Job	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Administrators may use to debug Schedule Task entries. Filter as needed.

Reference

• https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Schtasks scheduling job on remote system

This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1053.005
• Last Updated: 2020-07-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schtasks_scheduling_job_on_remote_system_filter`

Associated Analytic Story

• Lateral Movement

• NOBELIUM Group

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1053.005	Scheduled Task	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log

version: 4

---

Schtasks used for forcing a reboot

This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1053.005
• Last Updated: 2020-12-07

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schtasks_used_for_forcing_a_reboot_filter`

Associated Analytic Story

• Windows Persistence Techniques

• Ransomware

How To Implement

To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1053.005	Scheduled Task	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log

version: 4

---

Script Execution via WMI

This search looks for scripts launched via WMI.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1047
• Last Updated: 2020-03-16

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name  
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `script_execution_via_wmi_filter` 

Associated Analytic Story

• Suspicious WMI Use

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.user

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1047	Windows Management Instrumentation	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, administrators may use wmi to launch scripts for legitimate purposes.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log

version: 3

---

SearchProtocolHost with no Command Line with Network

The following analytic identifies searchprotocolhost.exe with no command line arguments and with a network connection. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1055
• Last Updated: 2021-04-19

details

Search

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| regex process="(searchprotocolhost\.exe.{0,4}$)" 
| join  process_id [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port 
| `drop_dm_object_name(Ports)` 
| rename  dest as connection_to_CNC] 
| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port 
| `searchprotocolhost_with_no_command_line_with_network_filter`

Associated Analytic Story

• Cobalt Strike

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes and ports node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest_port

• process_path

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives may be present in small environments. Tuning may be required based on parent process.

Reference

• https://github.com/fireeye/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_searchprotocolhost.log

version: 1

---

SecretDumps Offline NTDS Dumping Tool

This analytic detects a potential usage of secretsdump.py tool for dumping credentials (ntlm hash) from a copy of ntds.dit and SAM.Security,SYSTEM registrry hive. This technique was seen in some attacker that dump ntlm hashes offline after having a copy of ntds.dit and SAM/SYSTEM/SECURITY registry hive.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1003.003
• Last Updated: 2021-05-26

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe" Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process = "*-bootkey*") by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `secretdumps_offline_ntds_dumping_tool_filter`

Associated Analytic Story

• Credential Dumping

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.parent_process

• Processes.dest Processes.user

• Processes.process_id

• Processes.process_guid

ATT&CK

ID	Technique	Tactic
T1003.003	NTDS	Credential Access

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://github.com/SecureAuthCorp/impacket/blob/master/examples/secretsdump.py

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log

version: 1

---

Services Escalate Exe

The following analytic identifies the use of svc-exe with Cobalt Strike. The behavior typically follows after an adversary has already gained initial access and is escalating privileges. Using svc-exe, a randomly named binary will be downloaded from the remote Teamserver and placed on disk within C:\Windows\400619a.exe. Following, the binary will be added to the registry under key HKLM\System\CurrentControlSet\Services\400619a\ with multiple keys and values added to look like a legitimate service. Upon loading, services.exe will spawn the randomly named binary from \\127.0.0.1\ADMIN$\400619a.exe. The process lineage is completed with 400619a.exe spawning rundll32.exe, which is the default spawnto_ value for Cobalt Strike. The spawnto_ value is arbitrary and may be any process on disk (typically system32/syswow64 binary). The spawnto_ process will also contain a network connection. During triage, review parallel procesess and identify any additional file modifications.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1548
• Last Updated: 2021-05-18

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe Processes.process_path=*admin$* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `services_escalate_exe_filter`

Associated Analytic Story

• Cobalt Strike

How To Implement

To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1548	Abuse Elevation Control Mechanism	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

• Privilege Escalation

Known False Positives

False positives should be limited as services.exe should never spawn a process from ADMIN$. Filter as needed.

Reference

• https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/

• https://attack.mitre.org/techniques/T1548/

• https://www.cobaltstrike.com/help-beacon

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log

version: 1

---

Set Default PowerShell Execution Policy To Unrestricted or Bypass

Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059.001
• Last Updated: 2020-11-06

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`

Associated Analytic Story

• Malicious PowerShell

• Credential Dumping

• HAFNIUM Group

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints.

Required field

• _time

• Registry.registry_path

• Registry.registry_key_name

• Registry.registry_value_name

• Registry.dest

ATT&CK

ID	Technique	Tactic
T1059.001	PowerShell	Execution

Kill Chain Phase

• Installation

• Actions on Objectives

Known False Positives

Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log

version: 6

---

Setting Credentials via DSInternals modules

This detection identifies illegal setting of credentials via DSInternals modules.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1068, T1078, T1098
• Last Updated: 2020-11-03

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• process_name

• parent_process_name

• _time

• process_path

• dest_user_id

• process

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/MichaelGrafnetter/DSInternals

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllDSInternalsModules.log

version: 1

---

Setting Credentials via Mimikatz modules

This detection identifies illegal setting of credentials via Mimikatz modules.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1068, T1078, T1098
• Last Updated: 2020-11-03

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::addsid/)=true OR match_regex(cmd_line, /(?i)CRYPTO::scauth/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/gentilkiwi/mimikatz

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllMimikatzModules.log

version: 1

---

Setting Credentials via PowerSploit modules

This detection identifies illegal setting of credentials via PowerSploit modules.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1068, T1078, T1098
• Last Updated: 2020-11-03

details

Search

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map([ "cmd_line", cmd_line]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

Required field

• dest_device_id

• dest_user_id

• process

• _time

ATT&CK

ID	Technique	Tactic
T1068	Exploitation for Privilege Escalation	Privilege Escalation
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation
T1098	Account Manipulation	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified.

Reference

• https://github.com/PowerShellMafia/PowerSploit

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllPowerSploitModulesWithOldNames.log

version: 1

---

Shim Database File Creation

This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1546.011
• Last Updated: 2020-12-08

details

Search

| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
|`drop_dm_object_name(Filesystem)` 
| `shim_database_file_creation_filter`

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

• Filesystem.file_hash

• Filesystem.file_path

• Filesystem.file_name

• Filesystem.dest

ATT&CK

ID	Technique	Tactic
T1546.011	Application Shimming	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log

version: 3

---

Shim Database Installation With Suspicious Parameters

This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1546.011
• Last Updated: 2020-11-23

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `shim_database_installation_with_suspicious_parameters_filter`

Associated Analytic Story

• Windows Persistence Techniques

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1546.011	Application Shimming	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log

version: 4

---

Short Lived Windows Accounts

This search detects accounts that were created and deleted in a short time period.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Change
• ATT&CK: T1136.001
• Last Updated: 2020-07-06

details

Search

| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name("All_Changes")` 
| search result_id = 4720 result_id=4726 
| transaction user connected=false maxspan=240m 
| table firstTime lastTime count user dest result_id 
| `short_lived_windows_accounts_filter`

Associated Analytic Story

• Account Monitoring and Controls

How To Implement

This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/

Required field

• _time

• All_Changes.result_id

• All_Changes.user

• All_Changes.dest

ATT&CK

ID	Technique	Tactic
T1136.001	Local Account	Persistence

Kill Chain Phase

Known False Positives

It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log

version: 2

---

Single Letter Process On Endpoint

This search looks for process names that consist only of a single letter.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1204.002
• Last Updated: 2020-12-08

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) 
| search process_name_length=5 AND endExe=1 
| table count, firstTime, lastTime, dest, user, process, process_name 
| `single_letter_process_on_endpoint_filter`

Associated Analytic Story

• DHS Report TA18-074A

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.process

• Processes.process_name

ATT&CK

ID	Technique	Tactic
T1204.002	Malicious File	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log

version: 3

---

Spectre and Meltdown Vulnerable Systems

The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Vulnerabilities
• ATT&CK:
• Last Updated: 2017-01-07

details

Search

| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest 
| `drop_dm_object_name(Vulnerabilities)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `spectre_and_meltdown_vulnerable_systems_filter`

Associated Analytic Story

• Spectre And Meltdown Vulnerabilities

How To Implement

The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified.

Required field

• _time

Kill Chain Phase

Known False Positives

It is possible that your vulnerability scanner is not detecting that the patches have been applied.

Reference

Test Dataset

version: 1

---

Spike in File Writes

The search looks for a sharp increase in the number of files written to a particular host

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-03-16

details

Search

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| eventstats max(_time) as maxtime 
| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time<relative_time(maxtime, "-1d@d"), count,null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-1d@d"), count, null))) as stdev by "dest" 
| eval upperBound=(avg+stdev*4), isOutlier=if((count > upperBound) AND num_data_samples >=20, 1, 0) 
| search isOutlier=1 
| `spike_in_file_writes_filter` 

Associated Analytic Story

• SamSam Ransomware

• Ryuk Ransomware

• Ransomware

How To Implement

In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system.

Required field

• _time

• Filesystem.action

• Filesystem.dest

Kill Chain Phase

• Actions on Objectives

Known False Positives

It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications.

Reference

Test Dataset

version: 3

---

Splunk Enterprise Information Disclosure

This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2018-06-14

details

Search

index=_internal sourcetype=splunkd_ui_access server-info 
| search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" 
| rename clientip as src_ip, splunk_server as dest 
| stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `splunk_enterprise_information_disclosure_filter`

Associated Analytic Story

• Splunk Enterprise Vulnerability CVE-2018-11409

How To Implement

The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives.

Required field

• _time

Kill Chain Phase

• Delivery

Known False Positives

Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information.

Reference

Test Dataset

version: 1

---

Sunburst Correlation DLL and Network Event

The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1203
• Last Updated: 2020-12-14

details

Search

(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) 
| eventstats dc(EventCode) AS dc_events 
| where dc_events=2 
| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host 
| rename host as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `sunburst_correlation_dll_and_network_event_filter` 

Associated Analytic Story

• NOBELIUM Group

How To Implement

This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days.

Required field

• _time

• EventCode

• ImageLoaded

• QueryName

ATT&CK

ID	Technique	Tactic
T1203	Exploitation for Client Execution	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

unknown

Reference

• https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html

Test Dataset

version: 1

---

Supernova Webshell

This search aims to detect the Supernova webshell used in the SUNBURST attack.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Web
• ATT&CK: T1505.003
• Last Updated: 2021-01-06

details

Search

| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s 
| `supernova_webshell_filter`

Associated Analytic Story

• NOBELIUM Group

How To Implement

To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model.

Required field

• _time

• Web.url

• Web.src

• Web.dest

• Web.vendor_product

• Web.user

• Web.http_user_agent

ATT&CK

ID	Technique	Tactic
T1505.003	Web Shell	Persistence

Kill Chain Phase

• Exfiltration

Known False Positives

There might be false positives associted with this detection since items like args as a web argument is pretty generic.

Reference

• https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html

• https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/

Test Dataset

version: 1

---

Suspicious Changes to File Associations

This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1546.001
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| join [
| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count from datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest 
| `drop_dm_object_name("Registry")` 
| table process_id dest registry_path]
| `suspicious_changes_to_file_associations_filter` 

Associated Analytic Story

• Suspicious Windows Registry Activities

• Windows File Extension and Association Abuse

How To Implement

To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes and Registry nodes.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1546.001	Change Default File Association	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions.

Reference

Test Dataset

version: 4

---

Suspicious Curl Network Connection

The following analytic identifies the use of a curl contacting suspicious remote domains to checkin to command and control servers or download further implants. In the context of Silver Sparrow, curl is identified contacting s3.amazonaws.com. This particular behavior is common with MacOS adware-malicious software.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1105
• Last Updated: 2021-02-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl Processes.process=s3.amazonaws.com by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_curl_network_connection_filter`

Associated Analytic Story

• Silver Sparrow

• Ingress Tool Transfer

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1105	Ingress Tool Transfer	Command and Control

Kill Chain Phase

• Actions on Objectives

Known False Positives

Unknown. Filter as needed.

Reference

• https://redcanary.com/blog/clipping-silver-sparrows-wings/

• https://marcosantadev.com/manage-plist-files-plistbuddy/

Test Dataset

version: 1

---

Suspicious DLLHost no Command Line Arguments

The following analytic identifies DLLHost.exe with no command line arguments. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1055
• Last Updated: 2021-02-23

details

Search

`sysmon` EventID=1 (process_name=dllhost.exe OR OriginalFileName=dllhost.exe) 
| regex CommandLine="(dllhost\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_dllhost_no_command_line_arguments_filter`

Associated Analytic Story

• Cobalt Strike

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• process_name

• OriginalFileName

• CommandLine

• dest

• User

• ParentImage

• ParentCommandLine

• process_path

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives may be present in small environments. Tuning may be required based on parent process.

Reference

• https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile

• https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log

version: 1

---

Suspicious Driver Loaded Path

This analytic will detect suspicious driver loaded paths. This technique is commonly used by malicious software like coin miners (xmrig) to register its malicious driver from notable directories where executable or drivers do not commonly exist. During triage, validate this driver is for legitimate business use. Review the metadata and certificate information. Unsigned drivers from non-standard paths is not normal, but occurs. In addition, review driver loads into ntoskrnl.exe for possible other drivers of interest. Long tail analyze drivers by path (outside of default, and in default) for further review.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1543.003
• Last Updated: 2021-04-29

details

Search

`sysmon` EventCode=6 ImageLoaded = "*.sys" NOT (ImageLoaded IN("*\\WINDOWS\\inf","*\\WINDOWS\\System32\\drivers\\*", "*\\WINDOWS\\System32\\DriverStore\\FileRepository\\*")) 
|  stats  min(_time) as firstTime max(_time) as lastTime count by  Computer ImageLoaded Hashes IMPHASH Signature Signed 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_driver_loaded_path_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Computer

• ImageLoaded

• Hashes

• IMPHASH

• Signature

• Signed

ATT&CK

ID	Technique	Tactic
T1543.003	Windows Service	Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives will be present. Some applications do load drivers

Reference

• https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/

• https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Suspicious Email - UBA Anomaly

This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA).

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: UEBA
• ATT&CK: T1566
• Last Updated: 2020-07-22

details

Search

|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model 
| `drop_dm_object_name(All_UEBA_Events)` 
| `drop_dm_object_name(UEBA_Anomalies)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_email___uba_anomaly_filter`

Associated Analytic Story

• Suspicious Emails

How To Implement

You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1566	Phishing	Initial Access

Kill Chain Phase

• Delivery

Known False Positives

This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender.

Reference

Test Dataset

version: 3

---

Suspicious Email Attachment Extensions

This search looks for emails that have attachments with suspicious file extensions.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Email
• ATT&CK: T1566.001
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Email")` 
| `suspicious_email_attachments` 
| `suspicious_email_attachment_extensions_filter` 

Associated Analytic Story

• Emotet Malware DHS Report TA18-201A

• Suspicious Emails

How To Implement

You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model.
Splunk Phantom Playbook Integration
If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk https://splunkbase.splunk.com/app/3411/, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.

Required field

• _time

• All_Email.file_name

• All_Email.src_user

• All_Email.message_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Delivery

Known False Positives

None identified

Reference

Test Dataset

version: 3

---

Suspicious File Write

The search looks for files created with names that have been linked to malicious activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2019-04-25

details

Search

| tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Filesystem)` 
| `suspicious_writes` 
| `suspicious_file_write_filter`

Associated Analytic Story

• Hidden Cobra Malware

How To Implement

You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor.

Required field

• _time

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate.

Reference

Test Dataset

version: 3

---

Suspicious GPUpdate no Command Line Arguments

The following analytic identifies gpupdate.exe with no command line arguments. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1055
• Last Updated: 2021-02-23

details

Search

`sysmon` EventID=1 (process_name=gpupdate.exe OR OriginalFileName=GPUpdate.exe) 
| regex CommandLine="(gpupdate\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_gpupdate_no_command_line_arguments_filter`

Associated Analytic Story

• Cobalt Strike

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• process_name

• OriginalFileName

• CommandLine

• dest

• User

• ParentImage

• ParentCommandLine

• process_path

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives may be present in small environments. Tuning may be required based on parent process.

Reference

• https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile

• https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log

version: 1

---

Suspicious Java Classes

This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2018-12-06

details

Search

`stream_http` http_method=POST http_content_length>1 
| regex form_data="(?i)java\.lang\.(?:runtime
|processbuilder)" 
| rename src_ip as src 
| stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_java_classes_filter`

Associated Analytic Story

• Apache Struts Vulnerability

How To Implement

In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro.

Required field

• _time

• http_method

• http_content_length

• src_ip

• url

• status

• http_user_agent

• src

• dest

Kill Chain Phase

• Exploitation

Known False Positives

There are no known false positives.

Reference

Test Dataset

version: 1

---

Suspicious MSBuild Rename

The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1127.001, T1036.003
• Last Updated: 2021-01-12

details

Search

`sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `suspicious_msbuild_rename_filter`

Associated Analytic Story

• Trusted Developer Utilities Proxy Execution MSBuild

• Cobalt Strike

• Masquerading - Rename System Utilities

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• OriginalFileName

• process_name

• Computer

• User

• parent_process_name

• process_path

• CommandLine

ATT&CK

ID	Technique	Tactic
T1127.001	MSBuild	Defense Evasion
T1036.003	Rename System Utilities	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive.

Reference

• https://lolbas-project.github.io/lolbas/Binaries/Msbuild/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md

• https://github.com/infosecn1nja/MaliciousMacroMSBuild/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log

version: 1

---

Suspicious MSBuild Spawn

The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1127.001
• Last Updated: 2021-01-12

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_msbuild_spawn_filter`

Associated Analytic Story

• Trusted Developer Utilities Proxy Execution MSBuild

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

• Processes.parent_process

• Processes.user

ATT&CK

ID	Technique	Tactic
T1127.001	MSBuild	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

Reference

• https://lolbas-project.github.io/lolbas/Binaries/Msbuild/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log

version: 1

---

Suspicious PlistBuddy Usage

The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\

• PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist
  Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

• Datamodel: Endpoint

• ATT&CK: T1543.001

• Last Updated: 2021-02-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=PlistBuddy (Processes.process=*LaunchAgents* OR Processes.process=*RunAtLoad* OR Processes.process=*true*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|  `suspicious_plistbuddy_usage_filter`

Associated Analytic Story

• Silver Sparrow

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1543.001	Launch Agent	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.

Reference

• https://redcanary.com/blog/clipping-silver-sparrows-wings/

• https://marcosantadev.com/manage-plist-files-plistbuddy/

Test Dataset

version: 1

---

Suspicious PlistBuddy Usage via OSquery

The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\

• PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \

• PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist
  Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

• Datamodel:

• ATT&CK: T1543.001

• Last Updated: 2021-02-22

details

Search

`osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" 
|  `suspicious_plistbuddy_usage_via_osquery_filter`

Associated Analytic Story

• Silver Sparrow

How To Implement

OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct.

Required field

• _time

• columns.cmdline

ATT&CK

ID	Technique	Tactic
T1543.001	Launch Agent	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.

Reference

• https://redcanary.com/blog/clipping-silver-sparrows-wings/

• https://marcosantadev.com/manage-plist-files-plistbuddy/

Test Dataset

version: 1

---

Suspicious Process File Path

The following analytic will detect a suspicious process running in a file path where a process is not commonly seen and is most commonly used by malicious softtware. This behavior has been used by adversaries where they drop and run an exe in a path that is accessible without admin privileges.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1543
• Last Updated: 2021-05-05

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\windows\\fonts\\*" OR Processes.process_path = "*\\windows\\temp\\*" OR Processes.process_path = "*\\users\\public\\*" OR Processes.process_path = "*\\windows\\debug\\*" OR Processes.process_path.file_path = "*\\Users\\Administrator\\Music\\*" OR Processes.process_path.file_path = "*\\Windows\\servicing\\*" OR Processes.process_path.file_path = "*\\Users\\Default\\*" OR Processes.process_path.file_path = "*Recycle.bin*" OR Processes.process_path = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path = "*\\temp\\*" by Processes.parent_process_name Processes.parent_process Processes.process_path Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_process_file_path_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.parent_process

• Processes.process_path

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1543	Create or Modify System Process	Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Administrators may allow execution of specific binaries in non-standard paths. Filter as needed.

Reference

• https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

Suspicious Reg exe Process

This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1112
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename parent_process_id as process_id 
|dedup process_id
| table process_id dest] 
| `suspicious_reg_exe_process_filter` 

Associated Analytic Story

• Windows Defense Evasion Tactics

• Disabling Security Tools

• DHS Report TA18-074A

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.parent_process_name

• Processes.process_name

• Processes.user

• Processes.parent_process_name

• Processes.dest

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1112	Modify Registry	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out.

Reference

• https://car.mitre.org/wiki/CAR-2013-03-001

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log

version: 4

---

Suspicious Regsvr32 Register Suspicious Path

Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.010
• Last Updated: 2021-01-28

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_regsvr32_register_suspicious_path_filter`

Associated Analytic Story

• Suspicious Regsvr32 Activity

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1218.010	Regsvr32	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues.

Reference

• https://attack.mitre.org/techniques/T1218/010/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md

• https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/

• https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5

• https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log

version: 1

---

Suspicious Rundll32 Rename

The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1218.011, T1036.003
• Last Updated: 2021-02-04

details

Search

`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `suspicious_rundll32_rename_filter`

Associated Analytic Story

• Suspicious Rundll32 Activity

• Masquerading - Rename System Utilities

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.

Required field

• _time

• EventID

• OriginalFileName

• process_name

• Computer

• User

• parent_process_name

• process_path

• CommandLine

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion
T1036.003	Rename System Utilities	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.

Reference

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log

version: 1

---

Suspicious Rundll32 StartW

The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.011
• Last Updated: 2021-02-04

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_rundll32_startw_filter`

Associated Analytic Story

• Suspicious Rundll32 Activity

• Cobalt Strike

• Trickbot

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed.

Reference

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://www.cobaltstrike.com/help-windows-executable

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32

• https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log

version: 1

---

Suspicious Rundll32 dllregisterserver

The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.011
• Last Updated: 2021-02-09

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_rundll32_dllregisterserver_filter`

Associated Analytic Story

• Suspicious Rundll32 Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names.

Reference

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32

• https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east

• https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt

• https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/

• https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log

version: 1

---

Suspicious Rundll32 no Command Line Arguments

The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1218.011
• Last Updated: 2021-02-09

details

Search

`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) 
| regex CommandLine="(rundll32\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_rundll32_no_command_line_arguments_filter`

Associated Analytic Story

• Suspicious Rundll32 Activity

• Cobalt Strike

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• process_name

• OriginalFileName

• CommandLine

• dest

• User

• ParentImage

• ParentCommandLine

• process_path

ATT&CK

ID	Technique	Tactic
T1218.011	Rundll32	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.

Reference

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32

• https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log

version: 1

---

Suspicious SQLite3 LSQuarantine Behavior

The following analytic identifies the use of a SQLite3 querying the MacOS preferences to identify the original URL the pkg was downloaded from. This particular behavior is common with MacOS adware-malicious software. Upon triage, review other processes in parallel for suspicious activity. Identify any recent package installations.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1074
• Last Updated: 2021-02-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sqlite3 Processes.process=*LSQuarantine* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_sqlite3_lsquarantine_behavior_filter`

Associated Analytic Story

• Silver Sparrow

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1074	Data Staged	Collection

Kill Chain Phase

• Actions on Objectives

Known False Positives

Unknown.

Reference

• https://redcanary.com/blog/clipping-silver-sparrows-wings/

• https://marcosantadev.com/manage-plist-files-plistbuddy/

Test Dataset

version: 1

---

Suspicious Scheduled Task from Public Directory

The following detection identifies Scheduled Tasks registering (creating a new task) a binary or script to run from a public directory which includes users\public, \programdata\ and \windows\temp. Upon triage, review the binary or script in the command line for legitimacy, whether an approved binary/script or not. In addition, capture the binary or script in question and analyze for further behaviors. Identify the source and contain the endpoint.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1053.005
• Last Updated: 2021-03-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*\\users\\public\\* OR Processes.process=*\\programdata\\* OR Processes.process=*windows\\temp*)  Processes.process=*/create* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `suspicious_scheduled_task_from_public_directory_filter`

Associated Analytic Story

• Ransomware

• Ryuk Ransomware

• Windows Persistence Techniques

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1053.005	Scheduled Task	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

• Privilege Escalation

Known False Positives

Limited false positives may be present. Filter as needed by parent process or command line argument.

Reference

• https://attack.mitre.org/techniques/T1053/005/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtasks/windows-sysmon.log

version: 1

---

Suspicious SearchProtocolHost no Command Line Arguments

The following analytic identifies searchprotocolhost.exe with no command line arguments. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1055
• Last Updated: 2021-02-23

details

Search

`sysmon` EventID=1 (process_name=searchprotocolhost.exe OR OriginalFileName=SearchProtocolHost.exe) 
| regex CommandLine="(searchprotocolhost\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_searchprotocolhost_no_command_line_arguments_filter`

Associated Analytic Story

• Cobalt Strike

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• process_name

• OriginalFileName

• CommandLine

• dest

• User

• ParentImage

• ParentCommandLine

• process_path

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

Limited false positives may be present in small environments. Tuning may be required based on parent process.

Reference

• https://github.com/fireeye/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log

version: 1

---

Suspicious microsoft workflow compiler rename

The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1127, T1036.003
• Last Updated: 2021-01-12

details

Search

`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `suspicious_microsoft_workflow_compiler_rename_filter`

Associated Analytic Story

• Trusted Developer Utilities Proxy Execution

• Cobalt Strike

• Masquerading - Rename System Utilities

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• EventID

• OriginalFileName

• process_name

• Computer

• User

• parent_process_name

• process_path

• CommandLine

ATT&CK

ID	Technique	Tactic
T1127	Trusted Developer Utilities Proxy Execution	Defense Evasion
T1036.003	Rename System Utilities	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive.

Reference

• https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log

version: 1

---

Suspicious microsoft workflow compiler usage

The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1127
• Last Updated: 2021-01-12

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_microsoft_workflow_compiler_usage_filter`

Associated Analytic Story

• Trusted Developer Utilities Proxy Execution

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.parent_process

• Processes.user

ATT&CK

ID	Technique	Tactic
T1127	Trusted Developer Utilities Proxy Execution	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM.

Reference

• https://lolbas-project.github.io/lolbas/Binaries/Msbuild/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log

version: 1

---

Suspicious msbuild path

The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1127.001, T1036.003
• Last Updated: 2021-01-12

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `suspicious_msbuild_path_filter`

Associated Analytic Story

• Trusted Developer Utilities Proxy Execution MSBuild

• Cobalt Strike

• Masquerading - Rename System Utilities

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.process_path

• Processes.dest

• Processes.parent_process

• Processes.user

ATT&CK

ID	Technique	Tactic
T1127.001	MSBuild	Defense Evasion
T1036.003	Rename System Utilities	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on.

Reference

• https://lolbas-project.github.io/lolbas/Binaries/Msbuild/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log

version: 1

---

Suspicious mshta child process

The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.005
• Last Updated: 2021-01-12

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_mshta_child_process_filter`

Associated Analytic Story

• Suspicious MSHTA Activity

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

• Processes.parent_process

• Processes.user

ATT&CK

ID	Technique	Tactic
T1218.005	Mshta	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

Reference

• https://github.com/redcanaryco/AtomicTestHarnesses

• https://redcanary.com/blog/introducing-atomictestharnesses/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log

version: 1

---

Suspicious mshta spawn

The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.005
• Last Updated: 2021-01-20

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_mshta_spawn_filter`

Associated Analytic Story

• Suspicious MSHTA Activity

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

• Processes.parent_process

• Processes.user

ATT&CK

ID	Technique	Tactic
T1218.005	Mshta	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

Reference

• https://codewhitesec.blogspot.com/2018/07/lethalhta.html

• https://github.com/redcanaryco/AtomicTestHarnesses

• https://redcanary.com/blog/introducing-atomictestharnesses/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log

version: 1

---

Suspicious wevtutil Usage

The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1070.001
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `suspicious_wevtutil_usage_filter`

Associated Analytic Story

• Windows Log Manipulation

• Ransomware

• Clop Ransomware

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.process_name

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1070.001	Clear Windows Event Logs	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-sysmon.log

version: 3

---

Suspicious writes to System Volume Information

This search detects writes to the 'System Volume Information' folder by something other than the System process.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1036
• Last Updated: 2020-07-22

details

Search

(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_writes_to_system_volume_information_filter`

Associated Analytic Story

• Collection and Staging

How To Implement

You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1036	Masquerading	Defense Evasion

Kill Chain Phase

Known False Positives

It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate.

Reference

Test Dataset

version: 2

---

Suspicious writes to windows Recycle Bin

This search detects writes to the recycle bin by a process other than explorer.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1036
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest 
| `drop_dm_object_name("Filesystem")`
| search [
| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest
| `drop_dm_object_name("Processes")` 
| table process_id dest] 
| `suspicious_writes_to_windows_recycle_bin_filter`

Associated Analytic Story

• Collection and Staging

How To Implement

To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the Endpoint datamodel in the Processes and Filesystem nodes.

Required field

• _time

• Filesystem.file_path

• Filesystem.file_name

• Filesystem.process_id

• Filesystem.dest

• Processes.user

• Processes.process_name

• Processes.parent_process_name

• Processes.process_id

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1036	Masquerading	Defense Evasion

Kill Chain Phase

Known False Positives

Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log

version: 4

---

System Information Discovery Detection

Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1082
• Last Updated: 2020-10-12

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| eventstats dc(process) as dc_processes_by_dest by dest 
| where dc_processes_by_dest > 2 
| stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `system_information_discovery_detection_filter`

Associated Analytic Story

• Discovery Techniques

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process

• Processes.user

• Processes.process_name

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1082	System Information Discovery	Discovery

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators debugging servers

Reference

• https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log

version: 1

---

System Process Running from Unexpected Location

An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\Windows\System32 or C:\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK: T1036
• Last Updated: 2020-08-25

details

Search

 $ssa_input = 
| from read_ssa_enriched_events() 
| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null));
$cond_1 = 
| from $ssa_input 
| where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe";
$cond_2 = 
| from $ssa_input 
| where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe";
$cond_3 = 
| from $ssa_input 
| where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe";
$cond_4 = 
| from $ssa_input 
| where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe";
$cond_5 = 
| from $ssa_input 
| where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe";
$cond_6 = 
| from $ssa_input 
| where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe";

| from $cond_1 
| union $cond_2 
| union $cond_3 
| union $cond_4 
| union $cond_5 
| union $cond_6 
| where process_path NOT LIKE "%\\windows\\system32%" OR process_path NOT LIKE "%\\windows\\syswow64%" 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(device, user), body=create_map(["process_path", process_path, "process_name", process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Windows Defense Evasion Tactics

• Masquerading - Rename System Utilities

How To Implement

Collect endpoint data such as sysmon or 4688 events.

Required field

• dest_device_id

• process_name

• _time

• dest_user_id

• process_path

ATT&CK

ID	Technique	Tactic
T1036	Masquerading	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

None

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/system_process_running_unexpected_location/windows-security.log

version: 2

---

System Processes Run From Unexpected Locations

This search looks for system processes that typically execute from C:\Windows\System32\ or C:\Windows\SysWOW64. This may indicate a malicious process that is trying to hide as a legitimate process.
This detection utilizes a lookup that is deduped system32 and syswow64 directories from Server 2016 and Windows 10.
During triage, review the parallel processes - what process moved the native Windows binary? identify any artifacts on disk and review. If a remote destination is contacted, what is the reputation?

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1036.003
• Last Updated: 2020-12-08

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_hash 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `is_windows_system_file` 
| `system_processes_run_from_unexpected_locations_filter`

Associated Analytic Story

• Suspicious Command-Line Executions

• Unusual Processes

• Ransomware

• Masquerading - Rename System Utilities

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_path

• Processes.user

• Processes.dest

• Processes.process_name

• Processes.process_id

• Processes.parent_process_name

• Processes.process_hash

ATT&CK

ID	Technique	Tactic
T1036.003	Rename System Utilities	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

This detection may require tuning based on third party applications utilizing native Windows binaries in non-standard paths.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml

• https://attack.mitre.org/techniques/T1036/003/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log

version: 6

---

TOR Traffic

This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Network_Traffic
• ATT&CK: T1071.001
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Traffic")` 
| `tor_traffic_filter`

Associated Analytic Story

• Prohibited Traffic Allowed or Protocol Mismatch

• Ransomware

• Command and Control

• NOBELIUM Group

How To Implement

In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated.

Required field

• _time

• All_Traffic.app

• All_Traffic.action

• All_Traffic.src_ip

• All_Traffic.dest_ip

• All_Traffic.dest_port

ATT&CK

ID	Technique	Tactic
T1071.001	Web Protocols	Command and Control

Kill Chain Phase

• Command and Control

Known False Positives

None at this time

Reference

Test Dataset

version: 2

---

Trickbot Named Pipe

this search is to detect potential trickbot infection through the create/connected named pipe to the system. This technique is used by trickbot to communicate to its c2 to post or get command during infection.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1055
• Last Updated: 2021-04-26

details

Search

`sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" 
| stats  min(_time) as firstTime max(_time) as lastTime count by Computer user_id EventCode PipeName signature Image process_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `trickbot_named_pipe_filter`

Associated Analytic Story

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name and pipename from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. .

Required field

• _time

• Computer

• user_id

• EventCode

• PipeName

• signature

• Image

• process_id

ATT&CK

ID	Technique	Tactic
T1055	Process Injection	Defense Evasion, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://labs.vipre.com/trickbot-and-its-modules/

• https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/namedpipe/windows-sysmon.log

version: 1

---

USN Journal Deletion

The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1070
• Last Updated: 2018-12-03

details

Search

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| search process="*deletejournal*" AND process="*usn*" 
| `usn_journal_deletion_filter`

Associated Analytic Story

• Windows Log Manipulation

• Ransomware

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

Required field

• _time

• Processes.process

• Processes.parent_process

• Processes.process_name

• Processes.user

• Processes.parent_process_name

• Processes.dest

ATT&CK

ID	Technique	Tactic
T1070	Indicator Removal on Host	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log

version: 2

---

Uncommon Processes On Endpoint

This search looks for applications on the endpoint that you have marked as uncommon.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1204.002
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| `uncommon_processes` 
|`uncommon_processes_on_endpoint_filter` 

Associated Analytic Story

• Windows Privilege Escalation

• Unusual Processes

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file uncommon_processes_default.csv to track various features of process names that are usually uncommon in most environments. Please consider updating uncommon_processes_local.csv to hunt for processes that are uncommon in your environment.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1204.002	Malicious File	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

None identified

Reference

Test Dataset

version: 4

---

Unified Messaging Service Spawning a Process

This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1190
• Last Updated: 2021-03-02

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="umworkerprocess.exe" OR Processes.parent_process_name="UMService.exe" (Processes.process_name!="wermgr.exe" OR Processes.process_name!="werfault.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `unified_messaging_service_spawning_a_process_filter`

Associated Analytic Story

• HAFNIUM Group

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1190	Exploit Public-Facing Application	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

Unknown. Tune out child processes as needed to limit volume of false positives.

Reference

• https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/

• https://www.microsoft.com/security/blog/2021/03/02/hafnium-targeting-exchange-servers/

• https://blog.rapid7.com/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_umservices.log

version: 1

---

Unload Sysmon Filter Driver

Attackers often disable security tools to avoid detection. This search looks for the usage of process fltMC.exe to unload a Sysmon Driver that will stop sysmon from collecting the data.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2020-07-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv*  by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
|`unload_sysmon_filter_driver_filter`
| table firstTime lastTime dest user count process_name process_id parent_process_name process

Associated Analytic Story

• Disabling Security Tools

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with unload_sysmon_filter_driver_filter macro, update this macro to filter out false positives.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log

version: 3

---

Unsigned Image Loaded by LSASS

This search detects loading of unsigned images by LSASS. Deprecated because too noisy.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1003.001
• Last Updated: 2019-12-06

details

Search

`sysmon` EventID=7 Image=*lsass.exe Signed=false 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `unsigned_image_loaded_by_lsass_filter` 

Associated Analytic Story

• Credential Dumping

How To Implement

This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named sysmon. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1003.001	LSASS Memory	Credential Access

Kill Chain Phase

• Actions on Objectives

Known False Positives

Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs.

Reference

• https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

Test Dataset

version: 1

---

Unsuccessful Netbackup backups

This search gives you the hosts where a backup was attempted and then failed.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2017-09-12

details

Search

`netbackup` 
| stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE 
| search MESSAGE="An error occurred, failed to backup." 
| `security_content_ctime(latestTime)` 
| rename COMPUTERNAME as dest, MESSAGE as signature 
| table latestTime, dest, signature 
| `unsuccessful_netbackup_backups_filter`

Associated Analytic Story

• Monitor Backup Solution

How To Implement

To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution.

Required field

• _time

Kill Chain Phase

Known False Positives

None identified

Reference

Test Dataset

version: 1

---

Unusually Long Command Line

Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez.

• Product: Splunk Behavioral Analytics
• Datamodel:
• ATT&CK:
• Last Updated: 2020-10-06

details

Search

 
| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null) 
| where cmd_line!=null and dest_user_id!=null 
| eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+)
|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) 
| select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input 
| adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 
| where label AND quantile>0.99 
| first_time_event input_columns=["dest_device_id", "cmd_line"] 
| where first_time_dest_device_id_cmd_line 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body=create_map(["cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();

Associated Analytic Story

• Unusual Processes

How To Implement

You must be ingesting sysmon endpoint data that monitors command lines.

Required field

• process_name

• _time

• dest_device_id

• dest_user_id

• process

Kill Chain Phase

• Actions on Objectives

Known False Positives

This detection may flag suspiciously long command lines when there is not sufficient evidence (samples) for a given process that this detection is tracking; or when there is high variability in the length of the command line for the tracked process. Also, some legitimate applications may use long command lines. Such is the case of Ansible, that encodes Powershell scripts using long base64. Attackers may use this technique to obfuscate their payloads.

Reference

Test Dataset

version: 1

---

Unusually Long Command Line

Command lines that are extremely long may be indicative of malicious activity on your hosts.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2020-12-08

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
|  eval processlen=len(process) 
| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest 
| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process 
| `unusually_long_command_line_filter` 
|eval threshold = 3 
| where maxlen > ((threshold*stdevperhost) + avgperhost)

Associated Analytic Story

• Suspicious Command-Line Executions

• Unusual Processes

• Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

• Ransomware

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model.

Required field

• _time

• Processes.user

• Processes.dest

• Processes.process_name

• Processes.process

Kill Chain Phase

• Actions on Objectives

Known False Positives

Some legitimate applications start with long command lines.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log

version: 5

---

Unusually Long Command Line - MLTK

Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2019-05-08

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| eval processlen=len(process) 
| search user!=unknown 
| apply cmdline_pdfmodel threshold=0.01 
| rename "IsOutlier(processlen)" as isOutlier 
| search isOutlier > 0 
| table firstTime lastTime user dest process_name process processlen count 
| `unusually_long_command_line___mltk_filter`

Associated Analytic Story

• Suspicious Command-Line Executions

• Unusual Processes

• Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

• Ransomware

How To Implement

You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.

Required field

• _time

• Processes.user

• Processes.dest

• Processes.process_name

• Processes.process

Kill Chain Phase

• Actions on Objectives

Known False Positives

Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model.

Reference

Test Dataset

version: 1

---

Unusually Long Content-Type Length

This search looks for unusually long strings in the Content-Type http header that the client sends the server.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2017-10-13

details

Search

`stream_http` 
| eval cs_content_type_length = len(cs_content_type) 
| where cs_content_type_length > 100 
| table endtime src_ip dest_ip cs_content_type_length cs_content_type url 
| `unusually_long_content_type_length_filter`

Associated Analytic Story

• Apache Struts Vulnerability

How To Implement

This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field.

Required field

• _time

• cs_content_type

• endtime

• src_ip

• dest_ip

• url

Kill Chain Phase

• Delivery

Known False Positives

Very few legitimate Content-Type fields will have a length greater than 100 characters.

Reference

Test Dataset

version: 1

---

W3WP Spawning Shell

This query identifies a shell, PowerShell.exe or Cmd.exe, spawning from W3WP.exe, or IIS. In addition to IIS logs, this behavior with an EDR product will capture potential webshell activity, similar to the HAFNIUM Group abusing CVEs, on publicly available Exchange mail servers. During triage, review the parent process and child process of the shell being spawned. Review the command-line arguments and any file modifications that may occur. Identify additional parallel process, child processes, that may highlight further commands executed. After triaging, work to contain the threat and patch the system that is vulnerable.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1505.003
• Last Updated: 2021-03-03

details

Search

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=w3wp.exe AND Processes.process_name=cmd.exe OR Processes.process_name=powershell.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `w3wp_spawning_shell_filter`

Associated Analytic Story

• HAFNIUM Group

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

• Processes.parent_process

ATT&CK

ID	Technique	Tactic
T1505.003	Web Shell	Persistence

Kill Chain Phase

• Exploitation

Known False Positives

Baseline your environment before production. It is possible build systems using IIS will spawn cmd.exe to perform a software build. Filter as needed.

Reference

• https://www.microsoft.com/security/blog/2020/02/04/ghost-in-the-shell-investigating-web-shell-attacks/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon.log

version: 1

---

WBAdmin Delete System Backups

This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1490
• Last Updated: 2021-01-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `wbadmin_delete_system_backups_filter`

Associated Analytic Story

• Ryuk Ransomware

• Ransomware

How To Implement

You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.parent_process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1490	Inhibit System Recovery	Impact

Kill Chain Phase

• Actions on Objectives

Known False Positives

Administrators may modify the boot configuration.

Reference

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md

• https://thedfirreport.com/2020/10/08/ryuks-return/

• https://attack.mitre.org/techniques/T1490/

• https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log

version: 1

---

WMI Permanent Event Subscription

This search looks for the creation of WMI permanent event subscriptions.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1047
• Last Updated: 2018-10-23

details

Search

`wmi` EventCode=5861 Binding 
| rex field=Message "Consumer =\s+(?<consumer>[^;
|^$]+)" 
| search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" 
| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| rename ComputerName as dest 
| `wmi_permanent_event_subscription_filter`

Associated Analytic Story

• Suspicious WMI Use

How To Implement

To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].

Required field

• _time

• EventCode

• Message

• consumer

• ComputerName

ATT&CK

ID	Technique	Tactic
T1047	Windows Management Instrumentation	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, administrators may use event subscriptions for legitimate purposes.

Reference

Test Dataset

version: 1

---

WMI Permanent Event Subscription - Sysmon

This search looks for the creation of WMI permanent event subscriptions.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1546.003
• Last Updated: 2020-12-08

details

Search

`sysmon` EventCode=21 
| rename host as dest 
| table _time, dest, user, Operation, EventType, Query, Consumer, Filter 
| `wmi_permanent_event_subscription___sysmon_filter`

Associated Analytic Story

• Suspicious WMI Use

How To Implement

To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.

Required field

• _time

• EventCode

• host

• user

• Operation

• EventType

• Query

• Consumer

• Filter

ATT&CK

ID	Technique	Tactic
T1546.003	Windows Management Instrumentation Event Subscription	Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

Although unlikely, administrators may use event subscriptions for legitimate purposes.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log

version: 2

---

WMI Temporary Event Subscription

This search looks for the creation of WMI temporary event subscriptions.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1047
• Last Updated: 2018-10-23

details

Search

`wmi` EventCode=5860 Temporary 
| rex field=Message "NotificationQuery =\s+(?<query>[^;
|^$]+)" 
| search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" 
| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query  
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `wmi_temporary_event_subscription_filter`

Associated Analytic Story

• Suspicious WMI Use

How To Implement

To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].

Required field

• _time

• EventCode

• Message

• query

ATT&CK

ID	Technique	Tactic
T1047	Windows Management Instrumentation	Execution

Kill Chain Phase

• Actions on Objectives

Known False Positives

Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events.

Reference

Test Dataset

version: 1

---

Wbemprox COM Object Execution

this search is designed to detect potential malicious process loading COM object to wbemprox.dll,

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1218.003
• Last Updated: 2021-06-02

details

Search

`sysmon` EventCode=7  ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll", "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe")) NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) 
| stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name Computer EventCode Signed ProcessId Hashes IMPHASH 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wbemprox_com_object_execution_filter`

Associated Analytic Story

• Ransomware

• Revil Ransomware

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Image

• ImageLoaded

• process_name

• Computer

• EventCode

• Signed

• ProcessId

• Hashes

• IMPHASH

ATT&CK

ID	Technique	Tactic
T1218.003	CMSTP	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

legitimate process that are not in the exception list may trigger this event.

Reference

• https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

• https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf2/windows-sysmon.log

version: 1

---

Web Fraud - Account Harvesting

This search is used to identify the creation of multiple user accounts using the same email domain name.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1136
• Last Updated: 2018-10-08

details

Search

`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" 
| rex field=cookie "form_key=(?<SessionID>\w+)" 
| rex field=form_data "login\[username\]=(?<Username>[^&
|^$]+)" 
| search Username=* 
| rex field=Username "@(?<email_domain>.*)" 
| stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain 
| where UniqueUsernames> 25 
| `web_fraud___account_harvesting_filter`

Associated Analytic Story

• Web Fraud Detection

How To Implement

We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream.

Required field

• _time

• http_content_type

• uri

• cookie

ATT&CK

ID	Technique	Tactic
T1136	Create Account	Persistence

Kill Chain Phase

• Actions on Objectives

Known False Positives

As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated.

Reference

• https://splunkbase.splunk.com/app/2734/

• https://splunkbase.splunk.com/app/1809/

Test Dataset

version: 1

---

Web Fraud - Anomalous User Clickspeed

This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2018-10-08

details

Search

`stream_http` http_content_type=text* 
| rex field=cookie "form_key=(?<session_id>\w+)" 
| streamstats window=2 current=1 range(_time) as TimeDelta by session_id 
| where TimeDelta>0 
|stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id 
| where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) 
| `web_fraud___anomalous_user_clickspeed_filter`

Associated Analytic Story

• Web Fraud Detection

How To Implement

Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.

Required field

• _time

• http_content_type

• cookie

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Actions on Objectives

Known False Positives

As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior.

Reference

• https://en.wikipedia.org/wiki/Session_ID

• https://en.wikipedia.org/wiki/Session_(computer_science)

• https://en.wikipedia.org/wiki/HTTP_cookie

• https://splunkbase.splunk.com/app/1809/

Test Dataset

version: 1

---

Web Fraud - Password Sharing Across Accounts

This search is used to identify user accounts that share a common password.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2018-10-08

details

Search

`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost*  
| rex field=form_data "login\[username\]=(?<Username>[^&
|^$]+)" 
| rex field=form_data "login\[password\]=(?<Password>[^&
|^$]+)" 
| stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password
|where UniqueUsernames>5 
| `web_fraud___password_sharing_across_accounts_filter`

Associated Analytic Story

• Web Fraud Detection

How To Implement

We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.

Required field

• _time

• http_content_type

• uri

Kill Chain Phase

Known False Positives

As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior.

Reference

• https://en.wikipedia.org/wiki/Session_ID

• https://en.wikipedia.org/wiki/Session_(computer_science)

• https://en.wikipedia.org/wiki/HTTP_cookie

• https://splunkbase.splunk.com/app/1809/

Test Dataset

version: 1

---

Web Servers Executing Suspicious Processes

This search looks for suspicious processes on all systems labeled as web servers.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1082
• Last Updated: 2019-04-01

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `web_servers_executing_suspicious_processes_filter`

Associated Analytic Story

• Apache Struts Vulnerability

How To Implement

You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security.

Required field

• _time

• Processes.dest_category

• Processes.process

• Processes.process_name

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1082	System Information Discovery	Discovery

Kill Chain Phase

• Actions on Objectives

Known False Positives

Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks.

Reference

Test Dataset

version: 1

---

Wermgr Process Connecting To IP Check Web Services

this search is designed to detect suspicious wermgr.exe process that tries to connect to known IP web services. This technique is know for trickbot and other trojan spy malware to recon the infected machine and look for its ip address without so much finger print on the commandline process. Since wermgr.exe is designed for error handling process of windows it is really suspicious that this process is trying to connect to this IP web services cause that maybe cause of some malicious code injection.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1590.005
• Last Updated: 2021-04-19

details

Search

`sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com", "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net") 
|  stats  min(_time) as firstTime max(_time) as lastTime count by  process_path process_name process_id QueryName QueryStatus QueryResults Computer EventCode 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wermgr_process_connecting_to_ip_check_web_services_filter`

Associated Analytic Story

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, dns query name process path , and query ststus from your endpoints like EventCode 22. If you are using Sysmon, you must have at least version 12 of the Sysmon TA.

Required field

• _time

• process_path

• process_name

• process_id

• QueryName

• QueryStatus

• QueryResults

• Computer

• EventCode

ATT&CK

ID	Technique	Tactic
T1590.005	IP Addresses	Reconnaissance

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://labs.vipre.com/trickbot-and-its-modules/

• https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log

version: 1

---

Wermgr Process Create Executable File

this search is designed to detect potential malicious wermgr.exe process that drops or create executable file. Since wermgr.exe is an application trigger when error encountered in a process, it is really un ussual to this process to drop executable file. This technique is commonly seen in trickbot malware where it injects it code to this process to execute it malicious behavior like downloading other payload

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1027
• Last Updated: 2021-04-19

details

Search

`sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe" 
| stats  min(_time) as firstTime max(_time) as lastTime count by  Image TargetFilename process_name dest EventCode ProcessId 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wermgr_process_create_executable_file_filter`

Associated Analytic Story

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of wermgr.exe may be used.

Required field

• _time

• Image

• TargetFilename

• process_name

• dest

• EventCode

• ProcessId

ATT&CK

ID	Technique	Tactic
T1027	Obfuscated Files or Information	Defense Evasion

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://labs.vipre.com/trickbot-and-its-modules/

• https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log

version: 1

---

Wermgr Process Spawned CMD Or Powershell Process

This search is designed to detect suspicious cmd and powershell process spawned by wermgr.exe process. This suspicious behavior are commonly seen in code injection technique technique like trickbot to execute a shellcode, dll modules to run malicious behavior.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1059
• Last Updated: 2021-04-19

details

Search

| tstats `security_content_summariesonly` values(Processes.process) as cmdline min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "wermgr.exe" Processes.process_name = "cmd.exe" OR Processes.process_name = "powershell.exe" by Processes.parent_process_name Processes.parent_process_id  Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wermgr_process_spawned_cmd_or_powershell_process_filter`

Associated Analytic Story

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.parent_process_name

• Processes.parent_process_id

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.process_guid

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1059	Command and Scripting Interpreter	Execution

Kill Chain Phase

• Exploitation

Known False Positives

unknown

Reference

• https://labs.vipre.com/trickbot-and-its-modules/

• https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log

version: 1

---

WinEvent Scheduled Task Created Within Public Path

The following query utilizes Windows Security EventCode 4698, A scheduled task was created, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed from a user writeable file path.
The search will return the first time and last time the task was registered, as well as the Command to be executed, Task Name, Author, Enabled, and whether it is Hidden or not.
schtasks.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
The following DLL(s) are loaded when schtasks.exe or TaskService is launched -taskschd.dll. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory.
Upon triage, identify the task scheduled source. Was it schtasks.exe or was it via TaskService. Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1053.005
• Last Updated: 2021-04-08

details

Search

`wineventlog_security` EventCode=4698 
| xmlkv Message 
| search Command IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*") 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winevent_scheduled_task_created_within_public_path_filter`

Associated Analytic Story

• Windows Persistence Techniques

• Ransomware

• Ryuk Ransomware

How To Implement

To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.

Required field

• _time

• dest

• Task_Name

• Description

• Command

ATT&CK

ID	Technique	Tactic
T1053.005	Scheduled Task	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Privilege Escalation

Known False Positives

False positives are possible if legitimate applications are allowed to register tasks in public paths. Filter as needed based on paths that are used legitimately.

Reference

• https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/

• https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698

• https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/

• https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN

• https://app.any.run/tasks/e26f1b2e-befa-483b-91d2-e18636e2faf3/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/taskschedule/windows-security.log

version: 1

---

WinEvent Scheduled Task Created to Spawn Shell

The following query utilizes Windows Security EventCode 4698, A scheduled task was created, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a native Windows shell (PowerShell, Cmd, Wscript, Cscript).
The search will return the first time and last time the task was registered, as well as the Command to be executed, Task Name, Author, Enabled, and whether it is Hidden or not.
schtasks.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
The following DLL(s) are loaded when schtasks.exe or TaskService is launched -taskschd.dll. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory.
Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1053.005
• Last Updated: 2021-04-12

details

Search

`wineventlog_security` EventCode=4698 
| xmlkv Message 
| search Command IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winevent_scheduled_task_created_to_spawn_shell_filter`

Associated Analytic Story

• Windows Persistence Techniques

• Ransomware

• Ryuk Ransomware

How To Implement

To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.

Required field

• _time

• dest

• Task_Name

• Description

• Command

ATT&CK

ID	Technique	Tactic
T1053.005	Scheduled Task	Execution, Persistence, Privilege Escalation

Kill Chain Phase

• Privilege Escalation

Known False Positives

False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately.

Reference

• https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/

• https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698

• https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/

• https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-security.log

version: 1

---

WinRM Spawning a Process

The following analytic identifies suspicious processes spawning from WinRM (wsmprovhost.exe). This analytic is related to potential exploitation of CVE-2021-31166. which is a kernel-mode device driver http.sys vulnerability. Current proof of concept code will blue-screen the operating system. However, http.sys used by many different Windows processes, including WinRM. In this case, identifying suspicious process create (child processes) from wsmprovhost.exe is what this analytic is identifying.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1190
• Last Updated: 2021-05-21

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsmprovhost.exe Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winrm_spawning_a_process_filter`

Associated Analytic Story

• Unusual Processes

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• Processes.dest

• Processes.user

• Processes.parent_process

• Processes.process_name

• Processes.process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1190	Exploit Public-Facing Application	Initial Access

Kill Chain Phase

• Exploitation

• Privilege Escalation

• Denial of Service

Known False Positives

Unknown. Add new processes or filter as needed. It is possible system management software may spawn processes from wsmprovhost.exe.

Reference

• https://github.com/SigmaHQ/sigma/blob/master/rules/windows/process_access/win_susp_shell_spawn_from_winrm.yml

• https://www.zerodayinitiative.com/blog/2021/5/17/cve-2021-31166-a-wormable-code-execution-bug-in-httpsys

• https://github.com/0vercl0k/CVE-2021-31166/blob/main/cve-2021-31166.py

Test Dataset

version: 1

---

Windows AdFind Exe

This search looks for the execution of adfind.exe with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1018
• Last Updated: 2020-12-16

details

Search

 
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `windows_adfind_exe_filter` 

Associated Analytic Story

• NOBELIUM Group

• Domain Trust Discovery

How To Implement

To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Processes.process

• Processes.dest

• Processes.user

• Processes.process_name

• Processes.parent_process

• Processes.process_id

• Processes.parent_process_id

ATT&CK

ID	Technique	Tactic
T1018	Remote System Discovery	Discovery

Kill Chain Phase

• Exploitation

Known False Positives

administrators rarely use adfind, usually not used for legitimate reasons

Reference

• https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/

• https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log

version: 1

---

Windows DisableAntiSpyware Registry

The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. This particular behavior is typically executed when an ransomware actor gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1562.001
• Last Updated: 2021-03-02

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000001)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `windows_disableantispyware_registry_filter`

Associated Analytic Story

• Ryuk Ransomware

• Windows Defense Evasion Tactics

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Registry node.

Required field

• _time

• Registry.registry_key_name

• Registry.registry_value_name

• Registry.dest

• Registry.user

• Registry.registry_path

ATT&CK

ID	Technique	Tactic
T1562.001	Disable or Modify Tools	Defense Evasion

Kill Chain Phase

• Delivery

Known False Positives

It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.

Reference

• https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log

version: 2

---

Windows Event Log Cleared

This search looks for Windows events that indicate one of the Windows event logs has been purged.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1070.001
• Last Updated: 2020-07-06

details

Search

(`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `windows_event_log_cleared_filter`

Associated Analytic Story

• Windows Log Manipulation

• Ransomware

• Clop Ransomware

How To Implement

To successfully implement this search, you need to be ingesting Windows event logs from your hosts.

Required field

• _time

• EventCode

• dest

ATT&CK

ID	Technique	Tactic
T1070.001	Clear Windows Event Logs	Defense Evasion

Kill Chain Phase

• Actions on Objectives

Known False Positives

It is possible that these logs may be legitimately cleared by Administrators.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log

version: 4

---

Windows Security Account Manager Stopped

The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1489
• Last Updated: 2020-11-06

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `windows_security_account_manager_stopped_filter`

Associated Analytic Story

• Ryuk Ransomware

How To Implement

You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

• Processes.process_name

• Processes.process

• Processes.dest

• Processes.user

ATT&CK

ID	Technique	Tactic
T1489	Service Stop	Impact

Kill Chain Phase

• Delivery

Known False Positives

SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified.

Reference

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log

version: 1

---

Windows connhost exe started forcefully

The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1059.003
• Last Updated: 2020-11-06

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `windows_connhost_exe_started_forcefully_filter`

Associated Analytic Story

• Ryuk Ransomware

How To Implement

You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1059.003	Windows Command Shell	Execution

Kill Chain Phase

• Delivery

Known False Positives

This process should not be ran forcefully, we have not see any false positives for this detection

Reference

Test Dataset

version: 1

---

Windows hosts file modification

The search looks for modifications to the hosts file on all Windows endpoints across your environment.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK:
• Last Updated: 2018-11-02

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem  by Filesystem.file_name Filesystem.file_path Filesystem.dest 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* 
| `drop_dm_object_name(Filesystem)` 
| `windows_hosts_file_modification_filter`

Associated Analytic Story

• Host Redirection

How To Implement

To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.

Required field

• _time

Kill Chain Phase

• Command and Control

Known False Positives

There may be legitimate reasons for system administrators to add entries to this file.

Reference

Test Dataset

version: 1

---

Winword Spawning Cmd

The following detection identifies Microsoft Word spawning cmd.exe. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path C:\Program Files\Microsoft Office\root\Office16 (version will vary). Cmd.exe spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line will indicate what is being executed. During triage, review parallel processes and identify any files that may have been written. It is possible that COM is utilized to trampoline the child process to explorer.exe or wmiprvse.exe.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-22

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winword.exe Processes.process_name=cmd.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winword_spawning_cmd_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

False positives should be limited, but if any are present, filter as needed.

Reference

• https://app.any.run/tasks/73af0064-a785-4c0a-ab0d-cde593fe16ef/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log

version: 1

---

Winword Spawning PowerShell

The following detection identifies Microsoft Word spawning PowerShell. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path C:\Program Files\Microsoft Office\root\Office16 (version will vary). PowerShell spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-12

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `winword_spawning_powershell_filter`

Associated Analytic Story

• Spearphishing Attachments

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

False positives should be limited, but if any are present, filter as needed.

Reference

• https://redcanary.com/threat-detection-report/techniques/powershell/

• https://attack.mitre.org/techniques/T1566/001/

• https://app.any.run/tasks/b79fa381-f35c-4b3e-8d02-507e7ee7342f/

• https://app.any.run/tasks/181ac90b-0898-4631-8701-b778a30610ad/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log

version: 1

---

Winword Spawning Windows Script Host

The following detection identifies Microsoft Winword.exe spawning Windows Script Host - cscript.exe or wscript.exe. Typically, this is not common behavior and not default with Winword.exe. Winword.exe will generally be found in the following path C:\Program Files\Microsoft Office\root\Office16 (version will vary). cscript.exe or wscript.exe default location is c:\windows\system32\ or c:windows\syswow64`. cscript.exe or wscript.exe spawning from Winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1566.001
• Last Updated: 2021-04-12

details

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winword_spawning_windows_script_host_filter`

Associated Analytic Story

• Spearphishing Attachment

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node.

Required field

• _time

• process_name

• process_id

• parent_process_name

• dest

• user

• parent_process_id

ATT&CK

ID	Technique	Tactic
T1566.001	Spearphishing Attachment	Initial Access

Kill Chain Phase

• Exploitation

Known False Positives

There will be limited false positives and it will be different for every environment. Tune by child process or command-line as needed.

Reference

• https://attack.mitre.org/techniques/T1566/001/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_wsh.log

version: 1

---

Write Executable in SMB Share

This search is to detect suspicious dropping or creating an executable file in known sensitive SMB share. This technique is commonly used for lateral movement like how trickbot try to infect other machine in the infected network. This detection catch the access event (FILE WRITE) access to a share.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1021.002
• Last Updated: 2021-04-23

details

Search

`wineventlog_security` EventCode=5145 Relative_Target_Name IN ("*.exe","*.dll") Object_Type=File Share_Name IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") Access_Mask= "0x2" 
| stats min(_time) as firstTime max(_time) as lastTime count by EventCode Share_Name Relative_Target_Name Object_Type Access_Mask user src_port Source_Address 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `write_executable_in_smb_share_filter`

Associated Analytic Story

• Trickbot

How To Implement

To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy.

Required field

• _time

• EventCode

• Share_Name

• Relative_Target_Name

• Object_Type

• Access_Mask

• user

• src_port

• Source_Address

ATT&CK

ID	Technique	Tactic
T1021.002	SMB/Windows Admin Shares	Lateral Movement

Kill Chain Phase

• Lateral Movement

Known False Positives

unknown

Reference

• https://labs.vipre.com/trickbot-and-its-modules/

• https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/exe_smbshare/windows-security.log

version: 1

---

XMRIG Driver Loaded

This analytic identifies XMRIG coinminer driver installation on the system. The XMRIG driver name by default is WinRing0x64.sys. This cpu miner is an open source project that is commonly abused by adversaries to infect and mine bitcoin.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• ATT&CK: T1543.003
• Last Updated: 2021-04-29

details

Search

`sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys" 
|  stats  min(_time) as firstTime max(_time) as lastTime count by  Computer ImageLoaded Hashes IMPHASH Signature Signed 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `xmrig_driver_loaded_filter`

Associated Analytic Story

• XMRig

How To Implement

To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

Required field

• _time

• Computer

• ImageLoaded

• Hashes

• IMPHASH

• Signature

• Signed

ATT&CK

ID	Technique	Tactic
T1543.003	Windows Service	Persistence, Privilege Escalation

Kill Chain Phase

• Exploitation

Known False Positives

False positives should be limited.

Reference

• https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/

Test Dataset

• https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log

version: 1

---

aws detect attach to role policy

This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2020-07-27

details

Search

`aws_cloudwatchlogs_eks` attach policy
| spath requestParameters.policyArn 
| table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate  
| `aws_detect_attach_to_role_policy_filter`

Associated Analytic Story

• AWS Cross Account Activity

How To Implement

You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs

Required field

• _time

• requestParameters.policyArn

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Lateral Movement

Known False Positives

Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies.

Reference

Test Dataset

version: 1

---

aws detect permanent key creation

This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2020-07-27

details

Search

`aws_cloudwatchlogs_eks` CreateAccessKey 
| spath eventName 
| search eventName=CreateAccessKey "userIdentity.type"=IAMUser 
| table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId 
|`aws_detect_permanent_key_creation_filter`

Associated Analytic Story

• AWS Cross Account Activity

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs

Required field

• _time

• eventName

• userIdentity.type

• sourceIPAddress

• userName userIdentity.type

• userAgent

• action

• status

• responseElements.accessKey.createDate

• esponseElements.accessKey.status

• responseElements.accessKey.accessKeyId

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Lateral Movement

Known False Positives

Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context.

Reference

Test Dataset

version: 1

---

aws detect role creation

This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2020-07-27

details

Search

`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* 
| table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate 
| `aws_detect_role_creation_filter`

Associated Analytic Story

• AWS Cross Account Activity

How To Implement

You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs

Required field

• _time

• event_name

• action

• userIdentity.type

• requestParameters.description

• sourceIPAddress

• userIdentity.principalId

• userIdentity.arn

• action

• event_name

• awsRegion

• http_user_agent

• mfa_auth

• msg

• requestParameters.roleName

• requestParameters.description

• responseElements.role.arn

• responseElements.role.createDate

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Lateral Movement

Known False Positives

CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases.

Reference

Test Dataset

version: 1

---

aws detect sts assume role abuse

This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2020-07-27

details

Search

`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role 
| table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate 
| `aws_detect_sts_assume_role_abuse_filter`

Associated Analytic Story

• AWS Cross Account Activity

How To Implement

You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs

Required field

• _time

• user_type

• userIdentity.sessionContext.sessionIssuer.type

• sourceIPAddress

• userIdentity.arn

• user_agent

• user_access_key

• status

• action

• requestParameters.roleName

• esponseElements.role.roleName

• esponseElements.role.createDate

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Lateral Movement

Known False Positives

Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse.

Reference

Test Dataset

version: 1

---

aws detect sts get session token abuse

This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1550
• Last Updated: 2020-07-27

details

Search

`aws_cloudwatchlogs_eks` ASIA  userIdentity.type=IAMUser
| spath eventName 
| search eventName=GetSessionToken 
| table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region 
| `aws_detect_sts_get_session_token_abuse_filter`

Associated Analytic Story

• AWS Cross Account Activity

How To Implement

You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs

Required field

• _time

• userIdentity.type

• eventName

• sourceIPAddress

• eventTime

• userIdentity.arn

• userName

• userAgent

• user_type

• status

• region

ATT&CK

ID	Technique	Tactic
T1550	Use Alternate Authentication Material	Defense Evasion, Lateral Movement

Kill Chain Phase

• Lateral Movement

Known False Positives

Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used.

Reference

Test Dataset

version: 1

---

gcp detect oauth token abuse

This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally.

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel:
• ATT&CK: T1078
• Last Updated: 2020-09-01

details

Search

`google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog 
|table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message  
| `gcp_detect_oauth_token_abuse_filter`

Associated Analytic Story

• GCP Cross Account Activity

How To Implement

You must install splunk GCP add-on. This search works with gcp:pubsub:message logs

Required field

• _time

ATT&CK

ID	Technique	Tactic
T1078	Valid Accounts	Defense Evasion, Initial Access, Persistence, Privilege Escalation

Kill Chain Phase

• Lateral Movement

Known False Positives

GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs.

Reference

• https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1

• https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2

Test Dataset

version: 1

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